international journal of current research review

International Journal of Current Research and Review

Discontinued in Scopus as of 2021

Subject Area and Category

• Biochemistry, Genetics and Molecular Biology (miscellaneous)
• Dentistry (miscellaneous)
• Health Professions (miscellaneous)

Radiance Research Academy

Publication type

09755241, 22312196

2014, 2019-2021

The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values.

The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. It is based on the idea that 'all citations are not created equal'. SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.

Evolution of the number of published documents. All types of documents are considered, including citable and non citable documents.

This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric.

Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal.

Evolution of the number of total citation per document and external citation per document (i.e. journal self-citations removed) received by a journal's published documents during the three previous years. External citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents.

International Collaboration accounts for the articles that have been produced by researchers from several countries. The chart shows the ratio of a journal's documents signed by researchers from more than one country; that is including more than one country address.

Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. those documents other than research articles, reviews and conference papers.

Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those not cited during the following year.

Evolution of the percentage of female authors.

Evolution of the number of documents cited by public policy documents according to Overton database.

Evoution of the number of documents related to Sustainable Development Goals defined by United Nations. Available from 2018 onwards.

Leave a comment

Name * Required

Email (will not be published) * Required

* Required Cancel

The users of Scimago Journal & Country Rank have the possibility to dialogue through comments linked to a specific journal. The purpose is to have a forum in which general doubts about the processes of publication in the journal, experiences and other issues derived from the publication of papers are resolved. For topics on particular articles, maintain the dialogue through the usual channels with your editor.

Follow us on @ScimagoJR Scimago Lab , Copyright 2007-2024. Data Source: Scopus®

Cookie settings

Cookie Policy

Legal Notice

Privacy Policy

International Journal of Current Research and Review Latest Publications

Total documents, published by radiance research academy.

• Latest Documents
• Most Cited Documents
• Contributed Authors
• Related Sources
• Related Keywords

A Study to Find the Effect of Short-Wave Diathermy, Cervical Traction and Mobilization on Pain and Range of Motion in Acute Locking of the Cervical Spine - An Interventional Study

Post covid lower limb axonal neuropathy – a case report, a cross-sectional study on menstrual coping among reproductive age group women in chengalpattu, tamil nadu, comparison of ultrasound guided versus nerve stimulator guided technique of supraclavicular brachial plexus block in patients undergoing upper limb surgeries, c-reactive protein and neutrophil lymphocyte ratio levels as predictive biomarkers for severity of covid-19 infection, a cross-sectional study on quality of life of menopausal women in rural area, chengalpattu district, tamil nadu, liquid chromatography- mass spectrometry method development and its validation for the estimation of favipiravir and remdesivir in the rat plasma, utility of fluorescence immunoassay in early diagnosis of dengue, the role of pulse oximetry as a screening tool for early detection of critical congenital heart disease in newborn, the potential role of carotenoid pigment isolated from a new rhodotorula species in ameliorating cerebral ischemic stroke experimentally, export citation format, share document.

International Journal of Current Science Research and Review publish original research work of multidisciplinary field of Science .The Journal is welcoming original Research Articles, Book Reviews, Commentaries, Reviewed Articles, Technical Notes

Impact Factor : 7.943

Author Desk

Call for paper.

• Author Guideline
• Publication Ethics
• Publication Process
• Plagiarism Policy
• Mode of Payment
• Manuscript Template
• Copyright form
• Journal Cover Page

Information

• Journal Indexing
• Author Instructions
• Publication Fee
• Volume 07 Issue 06 June 2024
• Volume 07 Issue 05 May 2024
• Volume 07 Issue 04 April 2024
• Volume 07 Issue 03 March 2024
• Volume 07 Issue 02 February 2024
• Volume 07 Issue 01 January 2024
• Volume 06 Issue 12 December 2023
• Volume 06 Issue 11 November 2023
• Volume 06 Issue 09 September 2023
• Volume 06 Issue 08 August 2023
• Volume 06 Issue 07 July 2023
• Volume 06 Issue 06 June 2023
• Volume 06 Issue 05 May 2023
• Volume 06 Issue 04 April 2023
• Volume 06 Issue 03 March 2023
• Volume 06 Issue 02 February 2023
• Volume 06 Issue 01 January 2023
• Volume 06 Issue 10 October 2023
• Volume 05 Issue 12 December 2022
• Volume 05 Issue 11 November 2022
• Volume 05 Issue 10 October 2022
• Volume 05 Issue 09 September 2022
• Volume 05 Issue 08 August 2022
• Volume 05 Issue 07 July 2022
• Volume 05 Issue 06 June 2022
• Volume 05 Issue 05 May 2022
• Volume 05 Issue 04 April 2022
• Volume 05 Issue 03 March 2022
• Volume 05 Issue 02 February 2022
• Volume 05 Issue 01 January 2022
• Volume 04 Issue 12 December 2021
• Volume 04 Issue 11 November 2021
• Volume 04 Issue 10 October 2021
• Volume 04 Issue 09 September 2021
• Volume 04 Issue 08 August 2021
• Volume 04 Issue 07 July 2021
• Volume 04 Issue 06 June 2021
• Volume 04 Issue 05 May 2021
• Volume 04 Issue 04 April 2021
• Volume 04 Issue 03 March 2021
• Volume 04 Issue 02 February 2021
• Volume 04 Issue 01 January 2021
• Volume 03 Issue 12 December 2020
• Volume 03 Issue 11 November 2020
• Volume 03 Issue 10 October 2020
• Volume 03 Issue 09 September 2020
• Volume 03 Issue 08 August 2020
• Volume 03 Issue 07 July 2020
• Volume 03 Issue 06 June 2020
• Volume 03 Issue 05 May 2020
• Volume 3 Issue 04 April 2020
• Volume 3 Issue 03 March 2020
• Volume 3 Issue 02 February 2020
• Volume 3 Issue 01 January 2020
• Volume 2 Issue 09 November 2019
• Volume 2 Issue 08 October 2019
• Volume 2 Issue 07 September 2019
• Volume 2 Issue 06 August 2019
• Volume 02 Issue 05 July 2019
• Volume 02 Issue 04 June 2019
• Volume 02 ISSUE 03 May 2019
• Volume 02 ISSUE 02 April 2019
• Volume 02 Issue 01 March 2019
• Volume 01 Issue 01 December 2018

International Journal of Current Science Research and Review

ISSN: 2581-8341

DOI: 10.47191/ijcsrr || Impact Factor: 7.943 || ASI-FACTOR: 3

IPI VALUE (2021): 2.90

Call For Paper Volume 7 Issue 06 June 2024 Submission Deadlines Last Date of Article Submission: 25.06.2024 Submit Online: Click Here to Submit Submit Offline: [email protected]

Publish original research work of multidisciplinary field of Science .The Journal is welcoming original Research Articles, Book Reviews, Commentaries, Reviewed Articles, Technical Notes, Snippets, Case Studies, Theses and Dissertations relevant to the fields of all subject . All types of articles submitted to the journal are double €“ blind peer reviewed and subject to be referred at the discretion of the Members of the Editorial Board for publication.

Editorial Policy

Authors should prepare their manuscripts consistent with the instructions given within the authors€™ guidelines. Manuscripts which don’t conform to the format and elegance of the Journal could also be returned to the authors for revision or rejected. The Journal reserves the proper to form any longer formal changes and language corrections necessary during a manuscript accepted for publication in order that it conforms to the formatting requirements of the Journal.

Peer review process

We follow a strict double-blind reviewing of the submitted works that’s we promise to hide always the identity of both the reviewers and therefore the author from one another . If any submitted article fails to satisfy primary standards, it’ll be rejected and therefore the author are going to be communicated the choice promptly. If the editors are satisfied, they’re going to select two or more reviewers for detailed consideration of the articles. The editors may advise the author to revise the article for publication.

Open access policy

This journal provides immediate open access to its content on the principle that creating research freely available to the public supports a greater global exchange of knowladge.

Frequency of publication

IJCSRR is published as monthly journal with 12 issues per year. IJCSRR publishing peer reviewed papers of International and National level conferences conducted by various research and academic institutions.

Last Date of Final Submission : 25th of Every Month

VOLUME 07 ISSUE 06 JUNE 2024

Vol 7 No 6(2024)

The Effect of Pressure, Opportunity, Rationalization, Religiousness on Fraud Behavior (PT. Mayora Indah Tbk Jatake 1)

Angel Gracia Yohanes, Temy Setiawan, Theresia Olivia

Page No. 3517-3525

Adaptation Factor Analysis Instrument of Interpersonal Support Evaluation List in Indonesian Version: Confirmatory Approach

Megarizky Hotmauli, Aip Badrujaman, Susi Fitri

Page No. 3526-3535

The Identity Crisis among Peoples from Mixed Parents from Countries under in Inter-States Conflicts: Case of Rwanda, D.R. Congo and Burundi

Safari Dieudonn辿, Cyuzuzo Alice, Mathias Twahirwa, PhD.

Page No. 3536-3545

Safeguarding Intellectual Property in the Post-Cultural Advancement Law Era in Indonesia: Preserving Traditional Knowledge

Mochammad Riansyah Andhika Putra

Page No. 3546-3555

Safeguarding Heritage: Preserving Traditional Knowledge in Global Patent Law

Muhamad Sigit Al Furqon

Page No. 3556-3566

Integrating ERM with HACCP for Enhanced Efficiency in SME Beverage Manufacturing: The Case of SME Beverage Manufacture Company in Rembang, Indonesia

Maria Kristina, Nur Budi Mulyono

Page No. 3567-3577

Training Development to Eliminate Competency Gap at Component Rebuild Section (PT LC)

Maxsen R. Popang, Achmad Fajar Hendarman

Page No. 3578-3591

Effect of Stretching Exercise on Reducing Musculoskeletal Disorders for Administration Staff at Hospital

Gurdani Yogisutanti, Riki Bagja Firmansyah, Fahmi Fuadah

Page No. 3592-3597

Advanced TRST01 ESG Scoring Model with Beta Based Financial Metrics and Machine Learning Techniques

Gurucharan Kottapalli, Prabir Mishra

Page No. 3598-3611

Application of the Analytical Hierarchy Process (AHP) for Innovative Technological Projects Evaluation and Prioritization in an FMCG Company

Gregorious Claus, Leo Aldianto

Page No. 3612-3622

The Development of PJBL-STEAM Learning Design to Improve the Student Creativity in Handling Waste: Utilizing Used Cardboard in Making Simple Miniature ATMs

Santi Ida Laeli, Dafik ., Tuti Purwoningsih

Page No. 3623-3635

Submit Article

Subject Area

Health and medical science.

• Health Professions

Social Science

• Art and Humanities

Engineering & Technology

• Chemical Engineering
• Mechanical Engineering

Life science

• Agriculture
• Environmental Science
• Immunology and Microbiology

Track your Article

Most read articles, 40503-gurdani-riki-fahmi-2.docx.

Authors Name:

View Article

VOLUME 7 ISSUE 06 JUNE 2024

Last date of paper submission: 25th June

Read More Submit Now

Area Covered: Multidisciplinary

Article Type: Research Articles

• > Case Study
• > Reviewed Articles
• > Short Notes
• > Book Reviews
• > Theses & Dissertations

Social Links

• Editorial Board
• Online Submission
• Current Issue

EDITORIAL BOARD

• INSTRUCTION TO AUTHOR
• Current Issue
• February 2024
• January 2024
• December 2023
• November, 2023
• October 2023
• September 2023
• August 2023
• February 2023
• January 2023
• December 2022
• November 2022
• October 2022
• September 2022
• August 2022
• February 2022
• January 2022
• December 2021
• November, 2021
• October, 2021
• September, 2021
• August, 2021
• February 2021
• January 2021
• CALL FOR PAPERS 2022
• Certificate
• Reviewers Form
• Indexing & Abstracting
• Editorial Board
• Submit Manuscript
• Instruction to Author
• Past Issues
• Call for papers/May 2024
• Ethics and Malpractice
• Conflict of Interest Statement
• Statement of Informed Consent
• Final Proof Correction and Submission
• Publication Ethics
• Peer review process
• Cover images -April-2024
• Quick Response - Contact Us

CERTIFICATE

Author information.

• Copyright Form
• Join Editor & Peer Reviewers
• Paper Submission
• Impact Factor
• Policy of Screening

IMPACT FACTOR 2021

Subject area.

• Life Sciences / Biology
• Architecture / Building Management
• Asian Studies
• Business & Management
• Computer Science
• Economics & Finance
• Engineering / Acoustics
• Environmental Science
• Agricultural Sciences
• Pharmaceutical Sciences
• General Sciences
• Materials Science
• Mathematics
• Nanotechnology & Nanoscience
• Nonlinear Science
• Chaos & Dynamical Systems
• Social Sciences & Humanities

Why Us? >>

• Open Access
• Peer Reviewed
• Rapid Publication
• Life time hosting
• Free promotion service
• Free indexing service
• More citations
• Search engine friendly

WELCOME TO IJCR

SJIF Scientific Impact Factor: 8.132 ;  ISI Impact Factor 2019-2020: 1.532, Doi: https://doi.org/10.24941/ijcr.2017

International Journal of Current Research, (IJCR) is an international double-blinded referred and peer-viewed monthly online academic research journal in all the streams. IJCR encourages new ideas and works in all fields and it publishes high-quality original papers, theory-based empirical papers, review papers, case reports, conference reports/papers, technology reports, book reviews, commentaries, events, and news. We welcome papers from both academicians and practitioners papers on theories, business models, conceptual paradigms, academic research, and consultancy projects. All subject areas of Biology and life sciences : Agriculture, Anatomy, Astrobiology, Behavior, Bioacoustics and etc., Computer and Information Sciences : Artificial intelligence, Catalogs, Computer applications, Computer architecture, and etc., Earth sciences: Atmospheric science, Earth, Geochemistry, Geography, Geology and etc., Ecology and environmental sciences : Aquatic environments, Biogeochemistry, Biogeography, Bioindicators, Conservation science and etc., Engineering and technology: Aerospace engineering, Architectural engineering, Automotive engineering, Bioengineering, Chemical engineering and etc., Medicine, health sciences and Dental : Anatomy, Anesthesiology, Cardiology, Cardiovascular medicine and etc., People and places: Demography, Geographical locations, Population groupings and etc., Physical sciences : Astronomical Sciences, Chemistry, Materials science, Mathematics, Physics and etc., Research and analysis methods : Animal studies, Bioassays and physiological analysis, Biological cultures and etc., Science policy: Bioethics, Open science, Research funding and etc., Social sciences: Anthropology, Archaeology, Economics and etc. Authors are cordially invited to submit the full-length paper, Original and unpublished research articles, based on theoretical or experimental works, are solicited for publication in the journal. Submission of an article implies that the work described has not been published previously (except in the form of an abstract or academic thesis) and is not under consideration for publication elsewhere.

• Submission Date: Open
• Publication Date: Open
• Free full-text availability of articles
• Email Notification of new issue release
• Search Articles by author name, titles, issues and volumes
• Open Access Journal
• The facility of Online Submission of Articles on the website

What's News

• Volume 16, Issue 04 is now available online
• Online Publication any time for Every Month
• Submit your Research articles/papers/case study at   [email protected] (or) [email protected]

Important Notes:

• SJIF Scientific Journal Impact Factor: 8.132
• Fast Publication and Indexing Journal
• All changes in published articles may be made only 7 days after the online release
• Corresponding authors are responsible for their own paper contents and authors have to refer and cite those contents which come from other papers thus, in case of plagiarism journal doesn't have any responsibility
• Post-publication removal of articles from the website is impossible and all inquiries won't be answered
• [email protected]
• For all communication regarding how to publish a paper, the status of the paper, and other general information.
• [email protected]
• For communication regarding submission acceptance of the papers. For all communication related to payment-related and clarification.

CALL FOR PAPERS

Online paypal payment, ijmce recommendation, monthly archive.

• November 2023

Advantages of IJCR

• Rapid Publishing
• Professional publishing practices
• Indexing in leading database
• High level of citation
• High Qualitiy reader base
• High level author suport

Plagiarism Detection

IJCR is following an instant policy on rejection those received papers with plagiarism rate of more than 20% . So, All of authors and contributors must check their papers before submission to making assurance of following our anti-plagiarism policies.

Identifiers

Linking ISSN (ISSN-L): 2347-3215

URL http://www.journals.excellentpublishers.com

Google https://www.google.com/search?q=ISSN+%222347-3215%22

Bing https://www.bing.com/search?q=ISSN+%222347-3215%22

Yahoo https://search.yahoo.com/search?p=ISSN%20%222347-3215%22

Pubmed https://pubmed.ncbi.nlm.nih.gov/?term=%222347-3215%22%5BJournal%5D&sort=

National Library of India http://nationallibraryopac.nvli.in/cgi-bin/koha/opac-search.pl?advsearch=1&idx=ns&q=2347-3215&weight_search=1&do=Search&sort_by=relevance

Resource information

Title proper: International journal of current research and academic review.

Abbreviated key-title: Int. j. curr. res. acad. rev.

Other variant title: IJCRAR

Original alphabet of title: Basic roman

Subject: UDC : 050

Subject: Serial publications, periodicals (as subject)

Publisher: Kancheepuram: Excelent Publishers

Dates of publication: 2013- 9999

Frequency: Monthly

Type of resource: Periodical

Language: English

Country: India

Medium: Online

Indexed by: TITLE DOI

Indexed by: ROAD

Indexed by: CROSSREF

Indexed by: FATCAT

Indexed by: WIKIDATA

Indexed by: OPENALEX

Indexed by: PUBMED

Record information

Type of record: Confirmed

Last modification date: 06/02/2021

ISSN Center responsible of the record: ISSN National Centre for India Please contact this ISSN Centre by clicking on it for any request or query concerning the publication

Record creation date: 04/10/2013

Original ISSN Centre: ISSN National Centre for India

downloads requested

Discover all the features of the complete ISSN records

Display mode x.

Labelled view

MARC21 view

UNIMARC view

Editorial Policy

Authors Info

Current Issue

Special Issue

Editorial Board

Welcome to IJCRR

IC Value (2018): 90.90

SJIF (2021): 6.66

IPI Factor : 4.02

Impact Factor (CiteFactor) (2020-21): 1.84

ISI Impact Factor (2021-2022): 2.176

IC Value (2019): 90.81

IC Value (2020): 91.47

SJIF (2020) = 7.893

IJCRR DOI Prefix (CrossRef): 10.31782/2231-2196

Information Matrix for the Analysis of Journals (ICDS = 9.6)

Peer Reviewed::Scientific

Frequency: Fortnightly

Language: English

Indexed and Abstracted in : Crossref, CAS Abstracts, Publons, Google Scholar, Open J-Gate, ROAD, Indian Citation Index (ICI), ResearchGATE, Ulrich's Periodicals Directory, WorldCat (World's largest network of library content and services)

Search Articles

Track manuscript

Readers around the world

Advertisement

3(9), September, 2011

Currentissuetitle(org_id);>.

Announcements

Dr. Pramod Kumar Manjhi joined Editor-in-Chief since July 2021 onwards

COPE guidelines for Reviewers

SCOPUS indexing: 2014, 2019 to 2021

Awards, Research and Publication incentive Schemes by IJCRR

Best Article Award: 

One article from every issue is selected for the ‘Best Article Award’. Authors of selected ‘Best Article’ are rewarded with a certificate. IJCRR Editorial Board members select one ‘Best Article’ from the published issue based on originality, novelty, social usefulness of the work. The corresponding author of selected ‘Best Article Award’ is communicated and information of award is displayed on IJCRR’s website. Drop a mail to [email protected] for more details.

Women Researcher Award:

This award is instituted to encourage women researchers to publish her work in IJCRR. Women researcher, who intends to publish her research work in IJCRR as the first author is eligible to apply for this award. Editorial Board members decide on the selection of women researchers based on the originality, novelty, and social contribution of the research work. The corresponding author of the selected manuscript is communicated and information is displayed on IJCRR’s website. Under this award selected women, the author is eligible for publication incentives. Drop a mail to [email protected] for more details.

Emerging Researcher Award:

‘Emerging Researcher Award’ is instituted to encourage student researchers to publish their work in IJCRR. Student researchers, who intend to publish their research or review work in IJCRR as the first author are eligible to apply for this award. Editorial Board members decide on the selection of student researchers for the said award based on originality, novelty, and social applicability of the research work. Under this award selected student researcher is eligible for publication incentives. Drop a mail to [email protected] for more details.

Best Article Award

List of Awardees

A Study by  Ese Anibor et al. " Evaluation of Temporomandibular Joint Disorders Among Delta State University Students in Abraka, Nigeria " from Vol 13 issue 16 received Emerging Researcher Award

A Study by Alkhansa Mahmoud et al. entitled " mRNA Expression of Somatostatin Receptors (1-5) in MCF7 and MDA-MB231 Breast Cancer Cells " from Vol 13 issue 06 received Emerging Researcher Award

Indexed and Abstracted in

Antiplagiarism Policy:   IJCRR strongly condemn and discourage practice of plagiarism. All received manuscripts have to pass through "Plagiarism Detection Software" test before Toto Macau forwarding for peer review. We consider "Plagiarism is a crime"

IJCRR Code of Conduct:   To achieve a high standard of publication, we adopt Good Publishing Practices (updated in 2022) which are inspired by guidelines provided by Committee on Publication Ethics (COPE) , Open Access Scholarly Publishers Association (OASPA) and International Committee of Medical Journal Editors (ICMJE)

Disclaimer:   International Journal of Current Research and Review (IJCRR) provides platform for researchers to publish and discuss their original research and review work. IJCRR can not be held responsible for views, opinions and written statements of researchers published in this journal.

International Journal of Current Research and Review (IJCRR) provides platform for researchers to publish and discuss their original research and review work. IJCRR can not be held responsible for views, opinions and written statements of researchers published in this journal

Useful links

148, IMSR Building, Ayurvedic Layout,         Near NIT Complex, Sakkardara,         Nagpur-24, Maharashtra State, India

[email protected]

[email protected]

Copyright © 2024 IJCRR. Specialized online journals by ubijournal .Website by Ubitech solutions

• Paper Archives
• Journal Indexing
• Research Conference
• Add Journal

Searching By

• Search More ...

Description

Last modified: 2010-08-30 09:46:26

• No Archives

Advertisement

International Journal of Research and Review

International Journal of Research and Review (IJRR)

Issn: 2349-9788 (online) issn: 2454-2237 (print).

International Journal of Research and Review (E-ISSN: 2349-9788; P-ISSN: 2454-2237)is a double-blind, Indexed peer-reviewed, open access international journal dedicated to promotion of research in multidisciplinary areas. We define Open Access-journals as journals that use a funding model that does not charge readers or their institutions for access. From the BOAI definition of "Open Access" users shall have the right to "read, download, copy, distribute, print, search, or link" to the full texts of articles. The journal publishes original research article from broad areas like Accountancy, Agriculture, Anthropology, Anatomy, Architecture, Arts, Biochemistry, Bioinformatics, Biology, Bioscience, Biostatistics, Biotechnology, Botany, Chemistry, Commerce, Computer Science, Dairy Technology, Dentistry, Ecology, Economics, Education, Engineering, Environmental Science, Food & Nutrition, Forensic Science, Forestry, Geology, Geography, Health Sciences, History, Home Science, Journalism & Mass Communication, Language, Law, Life Science, Literature, Management, Marine Science, Mathematics, Medical Science, Microbiology, Pathology, Paramedical Science, Pharmacy, Philosophy, Physical Education, Physiotherapy, Physics, Political Science, Public Health, Psychology, Science, Social Science, Sociology, Sports Medicine, Statistics, Tourism, Veterinary Science & Animal Husbandry, Yoga, Zoology etc.

The International Journal of Research and Review (IJRR) provides rapid publication of articles in all areas of research.

Frequency: Monthly Language: English

Scope of Journal

International Journal of Research and Review (IJRR) is a double-blind Indexed peer-reviewed open access journal which publishes original articles, reviews and short communications that are not under consideration for publication elsewhere. The journal publishes papers based on original research that are judged by critical reviews, to make a substantial contribution in the field. It aims at rapid publication of high quality research results while maintaining rigorous review process. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and academic excellence. Papers are published approximately one month after acceptance.

IJRR is dedicated to promote high quality research work in multidisciplinary field.

Index Copernicus Value (ICV) for 2022: 100.00; ICV 2021: 100.00; ICV 2020: 100.00; ICV 2019: 96.58; ICV 2018: 95.79; ICV 2017: 85.08; ICV 2016: 67.22; ICV 2015: 73.64; ICV 2014: 52.85

Salient features of our journal.

✵ Journal is indexed with Index Copernicus and other indexing agencies.

✵ Print version (hard copy) with P-ISSN number is available.

✵ Fast Publication Process.

✵ Free e-Certificate of Publication on author's request.

How to Submit Manuscript:

Thesis publication.

Research Articles

Nitin V Vichare, Rakesh Maggon

Smita Deshkar, Niranjan Patil, Pranali Balmiki

Ashish Choudhary, Riyaz Farooq, Aamir Rashid Purra, Fayaz Ahmed Ahangar

Joshi Medhavi H., Desai Devangi S, Singh Lalli M, Joshi Vaibhavi A

Jasvir Singh

Mariya Thomas Chacko, Tima Babu, Jenin Vincent Parambi, Irene T. Ivan, Sheik Haja Sherief

Click Here to Read more...

Anwar Sholeh, Alwi Thamrin Nasution, Radar Radius Tarigan

Arnaldi Fernando, Dairion Gatot, Yan Indra Fajar Sitepu

Iji Clement O, Age Terungwa James

Varsha S. Dhage, Pratibha Chougule

Uzma Khan, Arindam Ghosh

Dr. Uzma Khan, Dr. Arindam Ghosh, Dr. John Pramod

Our Journals

Visit Website

International Journal of Science and Healthcare Research

Galore International Journal of Health Sciences and Research

Galore International Journal of Applied Sciences and Humanities

International Journal of

Current pharmaceutical review and research, e-issn: 0976 822x, nmc approved, peer review journal.

• Volume16,Issue6
• Volume16,Issue5
• Volume16,Issue4
• Volume16,Issue3
• Volume16,Issue2
• Volume16,Issue1
• Volume15,Issue12
• Volume15,Issue11
• Volume15,Issue10
• Volume15,Issue9
• Volume15,Issue8
• Volume15,Issue7
• Volume15,Issue6
• Volume15,Issue5
• Volume15,Issue4
• Volume15,Issue3
• Volume15,Issue2
• Volume15,Issue1
• Volume14,Issue4
• Volume14,Issue3
• Volume14,Issue2
• Volume14,Issue1
• Volume13,Issue4
• Volume13,Issue3
• Volume13,Issue2
• Volume13,Issue1
• Volume12,Issue4
• Volume12,Issue3
• Volume12,Issue2
• Volume12,Issue1
• Volume11,Issue4
• Volume11,Issue3
• Volume11,Issue2
• Volume11,Issue1
• Volume10,Issue4
• Volume10,Issue3
• Volume10,Issue2
• Volume10,Issue1
• Volume9,Issue6
• Volume9,Issue5
• Volume9,Issue4
• Volume9,Issue3
• Volume9,Issue2
• Volume9,Issue1
• Volume8,Issue6
• Volume8,Issue5
• Volume8,Issue4
• Volume8,Issue3
• Volume8,Issue2
• Volume8,Issue1
• Volume7,Issue6
• Volume7,Issue5
• Volume7,Issue4
• Volume7,Issue3
• Volume7,Issue2
• Volume7,Issue1
• Volume6,Issue6
• Volume6,Issue5
• Volume6,Issue4
• Volume6,Issue3
• Volume6,Issue2
• Volume6,Issue1
• Volume5,Issue4
• Volume5,Issue3
• Volume5,Issue2
• Volume5,Issue1
• Volume4,Issue4
• Volume4,Issue3
• Volume4,Issue2
• Volume4,Issue1
• Volume3,Issue4
• Volume3,Issue3
• Volume3,Issue2
• Volume3,Issue1
• Volume2,Issue4
• Volume2,Issue3
• Volume2,Issue2
• Volume2,Issue1
• Volume1,Issue3
• Volume1,Issue2
• Volume1,Issue1
• Join as Reviewer
• Join Editorial Board
• Editorial Board
• Instructions
• Submit Manuscript

Disclimer: Scopus and Crossref are registered trademark of respective companies.

This journal is member of Crossref. 

ABOUT IJCPR JOURNAL

INTERNATIONAL JOURNAL OF CURRENT PHARMACEUTICAL REVIEW AND RESEARCH is a monthly (from January 2023 onwards) published peer reviewed journal. It is to serve the interests of the research-oriented and  professional  section in the fields of medical, biomedical studies, clinical and pharmaceutical sciences. The Journal publishes original research work either as a Full Original Research Paper, Case Study or as Short Communication.  Review Articles  on a current topic in the said fields are also considered for publication by the Journal. Pharamaceutical, Chemical, Medical and Clinical Sciences like Analytical Chemistry, Phytochemistry, Pharmaceutics, Pharmaceutical & Medicinal Chemistry, Pharmacology, Pharmacognosy, Pharmaceutical Analysis, Computational Chemistry & Molecular Modeling/Drug Design including Pharmacokinetics, Pharmacodynamics, Pharmacoinformatics, Pharmacovigilance, Chemoinformatics and Pharmacogenomics, Anatomy, Physiology, Anesthesia, Biochemistry, Molecular Biology, Cell Biology, Genetics, Hematology, Pathology, Immunology, Microbiology, Virology, Parasitology, Surgery, Dental Sciences, Sports Physiology, Histopathology, Toxicology and all major disciplines of Biomedical Sciences, Pharmacokinetics, Molecular Drug Design, Phytochemistry, Toxicology, Biomedical Analysis, Clinical Research, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology, Agriculture, Herbal Medicine, Orthopaedics, Community Medicine, Pharmacology, General Medicine, ENT, Ophthalmology, Obstetrics and Gynaecology, Pediatric, forensic Medicine, psychiatry, TB and chest, Skin and VD, Radiodiagnosis,  Oncology, Surgery, Urology, Plastic surgery . and all major disciplines of Medical and Biomedical Studies . Manuscripts submitted to the journal must represent reports of original research, and the original data must be available for review by the editor if necessary.

All authors submitting of a manuscript to IJCPR must have agreed to its submission and are responsible for its content SildBerlin , including appropriate citations and acknowledgments, and must also have agreed that the corresponding author has the authority to act on their behalf in all matters pertaining to publication of the manuscript.

Embase Section Headings

The Publishing House (IJCPR) is committed to the timely publication of peer-reviewed articles in journals. The Publishing House requires all authors to comply fully with current ethical standards for publication in their disciplines. Manuscripts submitted to the journal must represent reports of original research, and the original data must be available for review by the editor if necessary. All authors of a manuscript must have agreed to its submission and are responsible for its content, including appropriate citations and acknowledgments, and must also have agreed that the corresponding author has the authority to act on their behalf in all matters pertaining to publication of the manuscript to IJCPR. By submission of a manuscript to the journal, the authors guarantee that they have the authority to publish the work and that the manuscript, or one with substantially the same content, was not published previously, and is not being considered for publication elsewhere other than IJCPR

When submitting papers for publication to IJCPR, it is expected that the authors will provide written assurance and describe the novelty of their work or in the approach taken in their research in a covering letter.

Authors must be careful when they reproduce text, tables or illustrations from other sources. Plagiarism will be viewed seriously. If any ethical concerns are raised against published manuscript along with proofs, then the editorial board will investigate the matter and can take decisions accordingly, the decision may include removal of manuscript as well as blacklisting of authors for future publication in the journal. The authors must understand the seriousness of above statement before submitting papers in IJPCR.

Manuscript can also be submitted to IJCPR by ONLINE  or sending email to [email protected] 

Impact Factor: 1.041

This journal is peer Reviewed Journal

• Call for paper
• Paper Submission
• Current Issue

Welcome at IJCRM !!!

International Journal of Current Research in Multidisciplinary (IJCRM) is a scholarly open access, peer-reviewed, interdisciplinary, monthly and fully refereed journal focusing on theories, methods and applications in all areas of Medical, Dental Science, Humanities & Social Science All relevant fields. .

The target of IJCRM is to disseminate FREE information and advertise research and mix with modern specialists with quality administration.

• Why Choose IJCRM
• Subscribe Alert
• Peer Review Process
• Impact Factor
• Paper Template
• Copy Right Form

Artificial Intelligence in Education: Implications for Policymakers, Researchers, and Practitioners

• Original research
• Open access
• Published: 04 June 2024

Cite this article

You have full access to this open access article

• Dirk Ifenthaler   ORCID: orcid.org/0000-0002-2446-6548 1 , 2 ,
• Rwitajit Majumdar 3 ,
• Pierre Gorissen 4 ,
• Miriam Judge 5 ,
• Shitanshu Mishra 6 ,
• Juliana Raffaghelli 7 &
• Atsushi Shimada 8  

One trending theme within research on learning and teaching is an emphasis on artificial intelligence (AI). While AI offers opportunities in the educational arena, blindly replacing human involvement is not the answer. Instead, current research suggests that the key lies in harnessing the strengths of both humans and AI to create a more effective and beneficial learning and teaching experience. Thus, the importance of ‘humans in the loop’ is becoming a central tenet of educational AI. As AI technology advances at breakneck speed, every area of society, including education, needs to engage with and explore the implications of this phenomenon. Therefore, this paper aims to assist in this process by examining the impact of AI on education from researchers’ and practitioners' perspectives. The authors conducted a Delphi study involving a survey administered to N  = 33 international professionals followed by in-depth face-to-face discussions with a panel of international researchers to identify key trends and challenges for deploying AI in education. The results indicate that the three most important and impactful trends were (1) privacy and ethical use of AI; (2) the importance of trustworthy algorithms; and (3) equity and fairness. Unsurprisingly, these were also identified as the three key challenges. Based on these findings, the paper outlines policy recommendations for AI in education and suggests a research agenda for closing identified research gaps.

Avoid common mistakes on your manuscript.

1 Introduction

Artificial intelligence (AI) is finding its way into people's everyday lives at breathtaking speed and with almost unlimited possibilities. Typical points of contact with AI include pattern, image and speech recognition, auto-completion or correction suggestions for digital search queries. Since the 1950s, AI has been recognised in computer science and interdisciplinary fields such as philosophy, cognitive science, neuroscience, and economics (Tegmark, 2018 ). AI refers to the attempt to develop machines that can do things that were previously only possible using human cognition (Zeide, 2019 ). In contrast to humans, however, AI systems can process much more data in real-time (De Laat et al., 2020 ).

AI in education represents a generic term to describe a wide collection of different technologies, algorithms, and related multimodal data applied in education's formal, non-formal, and informal contexts. It involves techniques such as data mining, machine learning, natural language processing, large language models (LLMs), generative models, and neural networks. The still-emerging field of AI in education has introduced new frameworks, methodological approaches, and empirical investigations into educational research; for example, novel methods in academic research include machine learning, network analyses, and empirical approaches based on computational modelling experiments (Bozkurt et al., 2021 ).

With the emerging opportunities of AI, learning and teaching may be supported in situ and in real-time for more efficient and valid solutions (Ifenthaler & Schumacher, 2023 ). Hence, AI has the potential to further revolutionise the integration of human and artificial intelligence and impact human and machine collaboration in learning and teaching (De Laat et al., 2020 ). The discourse around the utilization of AI in education shifted from being narrowly focused on automation-based tasks to the augmentation of human capabilities linked to learning and teaching (Chatti et al., 2020 ). Notably, the concept of ‘humans in the loop’ (U.S. Department of Education, 2023 ) has gained more traction in recent education discourse as concerns about ethics, risks, and equity emerge.

Due to the remaining challenges of implementing meaningful AI in educational contexts, especially for more sophisticated tasks, the reciprocal collaboration of humans and AI might be a suitable approach for enhancing the capacities of both (Baker, 2016 ). However, the importance of understanding how AI, as a stakeholder among humans, selects and acquires data in the process of learning and knowledge creation, learns to process and forget information, and shares knowledge with collaborators is yet to be empirically investigated (Al-Mahmood, 2020 ; Zawacki-Richter et al., 2019 ).

This paper is based on (a) a literature review focussing on the impact of AI in the context of education, (b) a Delphi study (Scheibe et al., 1975 ) involving N  = 33 international professionals and a focus discussion on current opportunities and challenges of AI as well as (c) outlining policy recommendations and (d) a research agenda for closing identified research gaps.

2 Background

2.1 artificial intelligence.

From a conceptual point of view, AI refers to the sequence and application of algorithms that enable specific commands to transform a data input into a data output. Following Graf Ballestrem et al. ( 2020 ), among several definitions related to AI (Sheikh et al., 2023 ), AI refers to a system that exhibits intelligent behaviour by analysing the environment and taking targeted measures to achieve specific goals using certain degrees of freedom. In this context, intelligent behaviour is associated with human cognition. The focus here is on human cognitive functions such as decision-making, problem-solving and learning (Bellman, 1978 ). AI is, therefore, a machine developed by humans that can achieve complex goals (partially) autonomously. By applying machine learning techniques, these machines can increasingly analyse the application environment and its context and adapt to changing conditions (De Laat et al., 2020 ).

Daugherty and Wilson ( 2018 ) analyse the interaction between humans and AI. They identified three fields of activity: (a) Human activities, such as leading teams, clarifying points of view, creating things, or assessing situations. The human activities remain an advantage for humans when compared to AI. (b) Activities performed by machines, such as carrying out processes and repeating them as required, forecasting target states, or adapting processes. The machine activities are regarded as an advantage when compared to humans. In between are the (c) human–machine alliances. In this alliance, people must develop, train, and manage AI systems—to empower them. In this alliance, machines extend the capabilities of humans to analyse large amounts of data from countless sources in (near) real time. In these alliances, humans and machines are not competitors. Instead, they become symbiotic partners that drive each other to higher performance levels. The paradigm shift from computers as tools to computers as partners is becoming increasingly differentiated in various fields of application (Wesche & Sonderegger, 2019 ), including in the context of education.

2.2 Artificial Intelligence in Education

Since the early 2010s, data and algorithms have been increasingly used in the context of higher education to support learning and teaching, for assessments, to develop curricula further, and to optimize university services (Pinkwart & Liu, 2020 ). A systematic review by Zawacki-Richter et al. ( 2019 ) identifies various fields of application for AI in the context of education: (a) modelling student data to make predictions about academic success, (b) intelligent tutoring systems that present learning artifacts or provide assistance and feedback, (c) adaptive systems that support learning processes and, if necessary, offer suggestions for learning support, and (d) automated examination systems for classifying learning achievements. In addition, (e) support functions are implemented in the area of pedagogical decisions by teachers (Arthars et al., 2019 ), and the (f) further development of course content and curricula (Ifenthaler, Gibson, et al., 2018 ).

However, there are only a few reliable empirical studies on the potential of AI in the context of education concerning its impact (Zawacki-Richter et al., 2019 ). System-wide implementations of the various AI application fields in the education context are also still pending (Gibson & Ifenthaler, 2020 ). According to analyses by Bates et al. ( 2020 ), AI remains a sleeping giant in the context of education. Despite the great attention paid to the topic of AI in educational organizations, the practical application of AI lags far behind the anticipated potential (Buckingham Shum & McKay, 2018 ). Deficits in organizational structures and a lack of personnel and technological equipment at educational organizations have been documented as reasons for this (Ifenthaler, 2017 ).

Despite its hesitant implementation, AI has far more potential to transform the education arena than any technology before it. Potentials for educational organizations made possible by AI include expanding access to education, increasing student success, improving student retention, lowering costs and reducing the duration of studies. The application of AI systems in the context of education can be categorized on various levels (Bates et al., 2020 ).

The first level is aimed at institutional processes. These include scalable applications for managing application and admission procedures (Adekitan & Noma-Osaghae, 2019 ) and AI-based support for student counselling and services (Jones, 2019 ). Another field of application is aimed at identifying at-risk students and preventing students from dropping out (Azcona et al., 2019 ; Hinkelmann & Jordine, 2019 ; Russell et al., 2020 ). For example, Hinkelmann and Jordine ( 2019 ) report an implementation of a machine learning algorithm to identify students-at-risk, based on their study behaviour. This information triggered a student counselling process, offering support for students toward meeting their study goals or understanding personal needs for continuing the study programme.

The second level aims to support learning and teaching processes. This includes the recommendation of relevant next learning steps and learning materials (Schumacher & Ifenthaler, 2021 ; Shimada et al., 2018 ), the automation of assessments and feedback (Ifenthaler, Grieff, et al., 2018 ), the promotion of reflection and awareness of the learning process (Schumacher & Ifenthaler, 2018 ), supporting social learning (Gašević et al., 2019 ), detecting undesirable learning behaviour and difficulties (Nespereira et al., 2015 ), identifying the current emotional state of learners (Taub et al., 2020 ), and predicting learning success (Glick et al., 2019 ). For instance, Schumacher and Ifenthaler ( 2021 ) successfully utilised different types of prompts related to their current learning process to support student self-regulation.

Furthermore, a third level, which encompasses learning about AI and related technologies, has also been identified (U.S. Department of Education, 2023 ). AI systems are also used for the quality assurance of curricula and the associated didactic arrangements (Ifenthaler, Gibson, et al., 2018 ) and to support teachers (Arthars et al., 2019 ). For example, Ifenthale, Gibson, et al. ( 2018 ) applied graph-network analysis to identify study patterns that supported re-designing learning tasks, materials, and assessments.

2.3 Ethics Related to Artificial Intelligence in Education

The tension between AI's potential and ethical principles in education was recognized early on (Slade & Prinsloo, 2013 ). Ifenthaler and Tracey ( 2016 ) continued the discourse on ethical issues, data protection, and privacy of data in the context of AI applications. The present conceptual and empirical contributions on ethics and AI in the context of education show that data protection and privacy rights are a central problem area in the implementation of AI (Li et al., 2023 ).

AI systems in the context of education are characterised by their autonomy, interactivity and adaptability. These properties enable effective management of the dynamic and often incompletely understood learning and teaching processes. However, AI systems with these characteristics are difficult to assess, and their predictions or recommendations can lead to unexpected behaviour or unwanted activities (i.e., black box). Richards and Dignum ( 2019 ) propose a value-centred design approach that considers ethical principles at every stage of developing and using AI systems for education. Following this approach, AI systems in the context of education must (a) identify relevant stakeholders; (b) identify stakeholders' values and requirements; (c) provide opportunities to aggregate the values and value interpretation of all stakeholders; (d) ensure linkage of values and system functionalities to support implementation decisions and sustainable use; (e) provide support in the selection of system components (from within or outside the organisation) against the background of ethical principles. Dignum ( 2017 ) integrates a multitude of ethical criteria into the so-called ART principles (Accountability, Responsibility, Transparency).

Education organisations must embrace the ART principles while implementing AI systems to ensure responsible, transparent and explainable use of AI systems. Initial study results indicate (Howell et al., 2018 ; Viberg et al., 2022 ; West, Heath, et al., 2016 ; West, Huijser, et al., 2016a , 2016b ) that students are not willing to disclose all data for AI applications despite anticipated benefits. Although a willingness to share learning-related data is signalled, personal information or social user paths are not. This remains a critical aspect, especially when implementing the many adaptive AI systems that rely on a large amount of data.

Future AI systems may take over decision-making responsibilities if they are integrated into education organisations' decision-making processes. For instance, this could happen if AI systems are used in automated examination or admissions processes (Prinsloo & Slade, 2014 ; Willis & Strunk, 2015 ; Willis et al., 2016 ). Education organisations and their stakeholders will, therefore, decide against the background of ethical principles whether this responsibility can be delegated to AI. At the same time, those involved in the respective education organisations must assess the extent to which AI systems can take responsibility (if any) for the decisions made.

2.4 Context and Research Questions

EDUsummIT is a UNESCO (United Nations Educational, Scientific and Cultural Organization; https://www.unesco.org ) endorsed global community of researchers, policy-makers, and practitioners committed to supporting the effective integration of Information Technology (IT) in education by promoting active dissemination and use of research. Approximately 90 leading researchers, policymakers, and practitioners from all continents and over 30 countries gathered in Kyoto, Japan, from 29 May to 01 June 2023, to discuss emerging themes and to define corresponding action items. Previous to the meeting, thematic working groups (TWGs) conducted research related to current challenges in educational technologies with a global impact. This paper is based on the work of the TWG, which focuses on ‘Artificial Intelligence for Learning and Teaching’. The authors of this article constituted the TWG.

The research questions addressed by the researchers of TWG ‘Artificial Intelligence for Learning and Teaching’ are as follows:

What recent research and innovations in artificial intelligence in education are linked to supporting learning, teaching, and educational decision-making?

What recommendations for artificial intelligence in education can be proposed for policy, practice, and research?

3 Delphi Study

This study aimed to uncover global trends and educational practices pertaining to AI in education. A panel of multinational specialists from industry and research institutions reached a consensus on a set of current trends using the Delphi method.

3.1 Methodology

The Delphi method is a robust approach for determining forecasts or policy positions considered to be the most essential (Scheibe et al., 1975 ). A Delphi study can be conducted using paper-and-pencil instruments, computer- or web-based approaches, as well as face-to-face communication processes. For this study, the researchers applied a mixed Delphi design, including (a) computer-based and (b) face-to-face discussion methods. In order to assure the reliability and validity of the current study, we closely followed the guidelines proposed by Beiderbeck et al. ( 2021 ), including the general phases of preparing, conducting, and analysing the Delphi study.

In the first phase, using the computer-based method, a panel of international researchers in artificial intelligence in education were invited to submit trends and institutional practices related to AI in the educational arena. The initial list consisted of N  = 70 trends. This initial list was then aggregated through agreement, eliminating duplicates and trends with similar meanings. Agreement on aggregated constructs was met through in-depth research debriefing and discussion among the involved researchers. The final consolidated list included N  = 20 topics of AI in education. In an additional step of the computer-based method, the list was disseminated to global specialists in AI in education. Each participant was asked to rate the 20 topics on the list concerning (1) importance, (2) impact, and (3) key challenges on a scale of 1–10 (with 10 being the highest). The instructions for the ratings were as follows:

Please rate the IMPORTANCE of each of the trends (on a scale of 10, where 10 is the highest IMPORTANCE) for learning and teaching related to AI in organizations within the next 3 years.

Please rate the IMPACT of each of the trends (on a scale of 10, where 10 is the highest IMPACT) on learning and teaching related to AI and how organizations will utilize them.

Please rate the KEY CHALLENGES of each of the trends in AI in education (on a scale of 10, where 10 is the highest CHALLENGE) that organizations will face within the next 3 years.

In preparation for the second phase, face-to-face discussion , the panel of international researchers were asked to provide three relevant scientific literature resources related to the identified key areas in the first phase and explain their contribution to the respective development area. Next, the panel of international researchers met face-to-face for a 3-day workshop. During the face-to-face meeting, the panel of international researchers and policymakers followed a discussion protocol made available before the meeting (Beiderbeck et al., 2021 ). Discussion questions included but were not limited to: (1) What new educational realities have you identified in AI in education so far? (2) What are recommendations for future educational realities in AI in education for practice, policy, and research? The panel of international researchers discussed and agreed on several trends, challenges, and recommendations concerning research gaps and important implications for educational stakeholders, including policymakers and practitioners.

3.2 Participants

The research team sent open invitations to recruit participants through relevant professional networks, conferences, and personal invitations. As a result, a convenience sample of N  = 33 participants (14 = female; 17 = male; 2 = undecided) with an average age of M  = 46.64 years ( SD  = 9.83) took part in the study. The global specialists were from research institutions ( n ri  = 26), industry ( n in  = 5), and government organizations ( n in  = 2). They had an average of M  = 17.8 years ( SD  = 9.4) of experience in research and development in educational technology and are currently focused on artificial intelligence. Participants were based in Argentina ( n  = 1), Australia ( n  = 3), Canada ( n  = 2), China ( n  = 1), Croatia ( n  = 1), Finland ( n  = 1), France ( n  = 1), Germany ( n  = 1), India ( n  = 1), Ireland ( n  = 3), Japan ( n  = 2), Philippines ( n  = 1), Spain ( n  = 2), Sweden ( n  = 1), The Netherlands ( n  = 6), UK ( n  = 4), and USA ( n  = 2).

3.3 Data Analysis

All data were saved and analysed using an anonymized process as per conventional research data protection procedures. Data were cleaned and combined for descriptive and inferential statistics using r Statistics ( https://www.r-project.org ). All effects were tested at the 0.05 significance level, and effect size measures were computed where relevant. Further, discussion protocols of the face-to-face discussion were transcribed and analysed using QCAmap, a software for qualitative content analysis (Mayring & Fenzl, 2022 ). Both inductive and deductive coding techniques were used (Mayring, 2015 ). Regular researcher debriefing was conducted during data analysis to enhance the reliability and validity of the quantitative and qualitative analysis. The deductive coding followed pre-established categories derived from theory and existing research findings as well as the initial list of trends (e.g., ethics and AI, diversity and inclusion). The inductive process included critical reflections on new realities that emerged since the project's initial phase (e.g., generative AI, LLMs).

4.1 Phase 1: Global Trends in Artificial Intelligence in Education

The first phase (i.e., computer-based method) resulted in a preliminary list of trends in AI in education. These trends were rated concerning importance (see Table  1 ), impact (see Table  2 ), and challenges (see Table  3 ).

As shown in Table  1 , the most important trends included (1) Privacy and ethical use of AI and big data in education ( M  = 8.7; SD  = 1.286), (2) Trustworthy algorithms for supporting education ( M  = 8.3; SD  = 1.608), and Fairness & equity of AI in education ( M  = 8.2; SD  = 1.674). Less important trends included (18) Generalization of AI models in education ( M  = 6.2; SD  = 2.018), (19) Intelligent and social robotics for education ( M  = 5.8; SD  = 2.335), and (20) Blockchain technology in education ( M  = 4.9; SD  = 2.482) (see Table  1 ).

Table 2 shows the most impactful trends, including (1) Privacy and ethical use of AI and big data in education ( M  = 8.2; SD  = 1.608), (2) Trustworthy algorithms for supporting education ( M  = 7.7; SD  = 2.268), and (3) Fairness & equity of AI in education ( M  = 7.7; SD  = 1.736). Less impactful trends included (18) Generalization of AI models in education ( M  = 6.4; SD  = 2.115), (19) Intelligent and social robotics for education ( M  = 5.5; SD  = 2.298), and (20) Blockchain technology in education ( M  = 5.0; SD  = 2.650) (see Table  2 ).

Challenges related to the trends in AI in education are presented in Table  3 . Key challenges included (1) Privacy and ethical use of AI and big data in education ( M  = 8.8; SD  = 1.455), (2) Trustworthy algorithms for supporting education ( M  = 8.3; SD  = 1.804), and (3) Fairness & equity of AI in education ( M  = 8.3; SD  = 1.855). Even the weakest challenges received ratings above the mean (18) Intelligent and social robotics for education ( M  = 7.0; SD  = 1.941), (19) Multimodal learning analytics in education ( M  = 6.9; SD  = 2.187), and (20) Blockchain technology in education ( M  = 6.6; SD  = 2.599) (see Table  3 ).

Overall, the challenges ( M  = 7.68, SD  = 0.315) of AI in education have been rated significantly higher than impact ( M  = 7.05, SD  = 0.593) and importance ( M  = 7.28, SD  = 0.829), F (2, 57) = 3.512, p  < 0.05, Eta2  = 0.110 (medium effect).

4.2 Phase 2: Consensus Related to Identified Areas of Artificial Intelligence in Education

For the second phase, the top three trends for importance, impact, and challenges of AI in education were critically reflected and linked with an in-depth and research-informed group discussion. However, all other trends have been recognized during the consensus phase and for developing recommendations toward strategies and actions. As shown in Table  4 , the panel of international researchers and policymakers agreed that (a) privacy and ethical use of AI and big data in education, (b) trustworthy algorithms for supporting education, and (c) fairness and equity of AI in education remain the key drivers of AI in education. Further, the panel of international researchers and policymakers identified emerging educational realities with AI, including (d) new roles of stakeholders in education, (e) human-AI-alliance in education, and (f) precautionary pre-emptive policies preceding practice for AI in education.

5 Discussion

This Delphi study included global specialists from research institutions, industry, and policymaking. The primary goal of the Delphi method is to structure a group discussion systematically. However, reaching a consensus in the discussion may also lead to a biased perspective on the research topic (Beiderbeck et al., 2021 ). Another limitation of the current study is the limited sample size. Hence, our convenience sample could have included more participants and further differentiated the various experience levels in AI in education. Hence, future studies may increase the empirical basis as well as the experience of participants related to AI in education. Further, a limitation may be seen in possible overlaps between the identified constructs during the Delphi study. However, through the in-depth face-to-face discussion of the panel of international researchers, the constructs were constantly monitored concerning their content validity and refined accordingly.

In summary, the highest-rated trends in AI in education regarding importance, impact, and challenges included privacy and ethical use of AI and big data in education, trustworthy algorithms for supporting education, and fairness and equity of AI in education. In addition, new roles of stakeholders in education, human-AI-alliance in education, and precautionary pre-emptive policies precede practice for AI in education have been identified as emerging realities of AI in education.

5.1 Trends Identified for AI in Education

Privacy and ethical use of AI and big data in education emphasise the importance of data privacy (data ownership, data access, and data protection) concerning the development, implementation, and use of AI systems in education. Inevitably, the handling of these data privacy issues has significant ethical implications for the stakeholders involved. For instance, Adejo and Connolly ( 2017 ) discuss ethical issues related to using learning analytics tools and technologies, focusing on privacy, accuracy, property, and accessibility concerns. Further, a survey study by Ifenthaler and Schumacher ( 2016 ) examined student perceptions of privacy principles in learning analytics systems. The findings show that students remained conservative in sharing personal data, and it was recommended that all stakeholders be involved in implementing learning analytics systems. Thus, the sustainable involvement of stakeholders increases trust and establishes transparency regarding the need for and use of data.

More recently, Celik ( 2023 ) focused on teachers' professional knowledge and ethical integration of AI-based tools in education and suggested that teachers with higher knowledge of interacting with AI tools have a better understanding of their pedagogical contributions. Accordingly, AI literacy among all stakeholders appears to be inevitable, including understanding AI capabilities, utilizing AI, and applying AI (Papamitsiou et al., 2021 ; Wang & Lester, 2023 ).

Trustworthy algorithms for supporting education focus on trustworthiness, which is defined as the security, reliability, validity, transparency, and accuracy of AI algorithms and the interpretability of the AI outputs used in education. It particularly focuses on the impact of algorithmic bias (systematic and repeated errors resulting in unfair outcomes) on different stakeholders and stages of algorithm development. Research has demonstrated that algorithmic bias is a problem for algorithms used in education (OECD, 2023 ). Bias, which can occur at all stages of the machine learning life cycle, is a multilayered phenomenon encompassing historical bias, representation bias, measurement bias, aggregation bias, evaluation bias and deployment bias (Suresh & Guttag, 2021 ). For instance, Baker and Hawn ( 2021 ) review algorithmic bias in education, discussing its causes and empirical evidence of its manifestation, focusing on the impacts of algorithmic bias on different groups and stages of algorithm development and deployment in education. Alexandron et al. ( 2019 ) raise concerns about reliability issues, identify the presence of fake learners who manipulate data, and demonstrate how their activity can bias analytics results. Li et al. ( 2023 ) also mention the inhibition of predictive fairness due to data bias in their systematic review of existing research on prediction bias in education. Minn et al. ( 2022 ) argue that it is challenging to extract psychologically meaningful explanations that are relevant to cognitive theory from large-scale models such as Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN), which have useful performance in knowledge tracking, and mention the necessity for simpler models to improve interpretability. On the contrary, such simplifications may result in limited validity and accuracy of the underlying models.

Fairness and equity of AI in education emphasises the need for explainability and accountability in the design of AI in education. It requires lawful, ethical, and robust AI systems to address technical and social perspectives. Current research related to the three trends overlaps and emphasises the importance of considering stakeholder involvement, professional knowledge, ethical guidelines, as well as the impact on learners, teachers, and organizations. For instance, Webb et al. ( 2021 ) conducted a comprehensive review of machine learning in education, highlighting the need for explainability and accountability in machine learning system design. They emphasised the importance of integrating ethical considerations into school curricula and providing recommendations for various stakeholders. Further, Bogina et al. ( 2021 ) focused on educating stakeholders about algorithmic fairness, accountability, transparency, and ethics in AI systems. They highlight the need for educational resources to address fairness concerns and provide recommendations for educational initiatives.

New roles of stakeholders in education is related to the phenomena that AI will be omnipresent in education, which inevitably involves stakeholders interacting with AI systems in an educational context. New roles and profiles are emerging beyond traditional ones. For instance, Buckingham Shum ( 2023 ) emphasises the need for enterprise-wide deployment of AI in education, which is accompanied by extensive staff training and support. Further, new forms of imagining AI and of deciding its integration into socio-cultural systems will have to be discussed by all stakeholders, particularly minority or excluded collectives. Hence, AI deployment reflects different levels of influence, partnership and adaptation that are required to introduce and sustain novel technologies in the complex system that constitutes an educational organisation. Further, Andrews et al. ( 2022 ) recommend appointing a Digital Ethics Officer (DEO) in educational organisations who would be responsible for overseeing ethical guidelines, controlling AI activities, ethics training, as well as creating an ethical awareness culture and advising management.

Human-AI-alliance in education emphasises that AI in education shifted from being narrowly focused on automation-based tasks to augmenting human capabilities linked to learning and teaching. Seeber et al. ( 2020 ) propose a research agenda to develop interrelated programs to explore the philosophical and pragmatic implications of integrating humans and AI in augmenting human collaboration. Similarly, De Laat et al. ( 2020 ) and Joksimovic et al. ( 2023 ) highlight the challenge of bringing human and artificial intelligence together so that learning in situ and in real-time will be supported. Multiple opportunities and challenges arise from the human-AI-alliances in education for educators, learners, and researchers. For instance, Kasneci et al. ( 2023 ) suggest educational content creation, improving student engagement and interaction, as well as personalized learning and teaching experiences.

Precautionary pre-emptive policies precede practice for AI in education, underlining that, overwhelmed by the rapid change in the technology landscape, decision-makers tend to introduce restrictive policies in reaction to initial societal concerns with emerging AI developments. Jimerson and Childs ( 2017 ) highlight the issue of educational data use and how state and local policies fail to align with the broader evidence base of educational organisations. As a reaction toward uninformed actions in educational organisations, Tsai et al. ( 2018 ) introduced a policy and strategy framework that may support large-scale implementation involving multi-stakeholder engagement and approaches toward needs analysis. This framework suggests various dimensions, including mapping the political context, identifying the key stakeholders, identifying the desired behaviour changes, developing an engagement strategy, analysing the capacity to effect change, and establishing monitoring and learning opportunities.

5.2 Strategies and Actions

Based on the findings of the Delphi study as well as current work by other researchers, we recommend the following actions for policymakers (PM), researchers (RE), and practitioners (PR), each strategy linked to the corresponding challenges identified above. A detailed implementation plan for the strategies and related stakeholders can be found in a related paper published during EDUsummIT ( https://www.let.media.kyoto-u.ac.jp/edusummit2022/ ):

In order to support the new roles of stakeholders in education

Identify the elements involved in the new roles (RE)

Identify and implement pedagogical practices for AI in education (PR, RE)

Develop policies to support AI and data literacies through curriculum development (PM)

In order to support the Human-AI-Alliance in education

Encourage and support collaborative interaction between stakeholders and AI systems in education (RE)

Take control of available AI systems and optimize teaching and learning strategies (PR)

Promote institutional strategies and actions in order to support teachers’ agency and avoid teachers’ de-professionalization (PM, PR)

In order to support evidence-informed practices of AI in education

Use both the results of fundamental research into AI and the results of live case studies to build a robust body of knowledge and evidence about AI in education (RE)

Support open science and research on AI in education (PM)

Implement evidence-informed development of AI applications (RE, PR)

Implement evidence-informed pedagogical practices (PR, RE)

In order to support ethical considerations of AI in education

Forefront privacy and ethical considerations utilizing a multi-perspective and interdisciplinary approach as the core of AI in education (PM, RE, PR)

Consider the context, situatedness, and complexity of AI in education’s impacts at the time of exploring ethical implications (PR)

Continuously study the effects of AI systems in the context of education (RE)

6 Conclusion

The evolution of Artificial Intelligence (AI) in education has witnessed a profound transformation over recent years, holding tremendous promise for the future of learning (Bozkurt et al., 2021 ). As we stand at the convergence of technology and education, the potential impact of AI is poised to reshape traditional educational paradigms in multifaceted ways. Through supporting personalised learning experiences, AI has showcased its ability to cater to individual student needs, offering tailored curricula and adaptive assessments (Brusilovsky, 1996 ; Hemmler & Ifenthaler, 2022 ; Jones & Winne, 1992 ; Martin et al., 2020 ). This customisability of education fosters a more inclusive and effective learning environment, accommodating diverse learning needs and regulations. Moreover, AI tools augment the role of educators by automating administrative tasks, enabling them to allocate more time to mentoring, fostering creativity, and critical thinking (Ames et al., 2021 ). However, the proliferation of AI in education also raises pertinent ethical concerns, including data privacy, algorithmic biases, and the digital divide (Baker & Hawn, 2021 ; Ifenthaler, 2023 ). Addressing these concerns requires a conscientious approach, emphasising transparency, equity, and responsible AI development and deployment. In addition, in recent years, the emergence of generative AI, such as ChatGPT, is expected to facilitate interactive learning and assist instructors, while concerns such as the generation of incorrect information and privacy issues are also being addressed (Baidoo-Anu & Owusu Ansah, 2023 ; Lo, 2023 ).

Looking forward, the future of AI in education holds tremendous potential for transformation of learning and teaching. Yet, realising the full potential of AI in education necessitates concerted efforts from stakeholders—educators, policymakers, technologists, and researchers—to collaborate, innovate, and navigate the evolving ethical and pedagogical considerations. Embracing AI's potential while safeguarding against its pitfalls will be crucial in harnessing its power to create a more equitable, accessible, and effective educational arena.

Data availability

The data that support the findings of this study are available from the authors upon reasonable request.

Adejo, O., & Connolly, T. (2017). Learning analytics in a shared-network educational environment: ethical issues and countermeasures. International Journal of Advanced Computer Science and Applications , 8 (4). https://doi.org/10.14569/IJACSA.2017.080404

Adekitan, A. I., & Noma-Osaghae, E. (2019). Data mining approach to predicting the performance of first year student in a university using the admission requirements. Education and Information Technologies, 24 , 1527–1543. https://doi.org/10.1007/s10639-018-9839-7

Article   Google Scholar  

Alexandron, G., Yoo, L., Ruipérez-Valiente, J. A., Lee, S., & Pritchard, D. (2019). Are MOOC learning analytics results trustworthy? With fake learners, they might not be! International Journal of Artificial Intelligence in Education, 29 , 484–506. https://doi.org/10.1007/s40593-019-00183-1

Al-Mahmood, R. (2020). The politics of learning analytics. In D. Ifenthaler & D. C. Gibson (Eds.), Adoption of data analytics in higher education learning and teaching (pp. 20–38). Springer.

Google Scholar  

Ames, K., Harris, L. R., Dargusch, J., & Bloomfield, C. (2021). ‘So you can make it fast or make it up’: K–12 teachers’ perspectives on technology’s affordances and constraints when supporting distance education learning. The Australian Educational Researcher, 48 , 359–376. https://doi.org/10.1007/s13384-020-00395-8

Andrews, D., Leitner, P., Schön, S., & Ebner, M. (2022). Developing a professional profile of a digital ethics officer in an educational technology unit in higher education. In P. Zaphiris & A. Ioannou (Eds.), Learning and collaboration technologies. Designing the learner and teacher experience. HCII 2022. Lecture notes in computer science (Vol. 13328, pp. 157–175). Springer. https://doi.org/10.1007/978-3-031-05657-4_12

Arthars, N., Dollinger, M., Vigentini, L., Liu, D. Y., Kondo, E., & King, D. M. (2019). Empowering teachers to personalize learning support. In D. Ifenthaler, D.-K. Mah, & J. Y.-K. Yau (Eds.), Utilizing learning analytics to support study success (pp. 223–248). Springer. https://doi.org/10.1007/978-3-319-64792-0_13

Azcona, D., Hsiao, I., & Smeaton, A. F. (2019). Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints. User Modeling and User-Adapted Interaction, 29 , 759–788. https://doi.org/10.1007/s11257-019-09234-7

Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): understanding the potential benefits of chatgpt in promoting teaching and learning. Journal of AI , 7 (1), 52–62. https://doi.org/10.61969/jai.1337500

Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26 , 60–6140. https://doi.org/10.1007/s40593-016-0105-0

Baker, R. S., & Hawn, A. (2021). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32 (4), 1052–1092. https://doi.org/10.1007/s40593-021-00285-9

Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17 (42), 1–12. https://doi.org/10.1186/s41239-020-00218-x

Beiderbeck, D., Frevel, N., von der Gracht, H. A., Schmidt, S. L., & Schweitzer, V. M. (2021). Preparing, conducting, and analyzing Delphi surveys: Cross-disciplinary practices, new directions, and advancements. MethodsX, 8 , 101401. https://doi.org/10.1016/j.mex.2021.101401

Bellman, R. (1978). An introduction to artificial intelligence: can computers think? . Boyd & Fraser.

Bogina, V., Hartman, A., Kuflik, T., & Shulner-Tal, A. (2021). Educating software and AI stakeholders about algorithmic fairness, accountability, transparency and ethics. International Journal of Artificial Intelligence in Education . https://doi.org/10.1007/s40593-021-00248-0

Bozkurt, A., Karadeniz, A., Bañeres, D., Guerrero-Roldán, A., & Rodríguez, M. E. (2021). Artificial intelligence and reflections from educational landscape: A review of ai studies in half a century. Sustainability, 13 , 800. https://doi.org/10.3390/su13020800

Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6 (2–3), 87–129. https://doi.org/10.1007/BF00143964

Buckingham Shum, S., & McKay, T. A. (2018). Architecting for learning analytics. Innovating for sustainable impact. EDUCAUSE Review , 53 (2), 25–37. https://er.educause.edu/articles/2018/3/architecting-for-learning-analytics-innovating-for-sustainable-impact

Buckingham Shum, S. (2023). Embedding learning analytics in a university: Boardroom, staff room, server room, classroom. In O. Viberg & Å. Grönlund (Eds.), Practicable learning analytics (pp. 17–33). Springer. https://doi.org/10.1007/978-3-031-27646-0_2

Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138 , 107468. https://doi.org/10.1016/j.chb.2022.107468

Chatti, M. A., Muslim, A., Guesmi, M., Richtscheid, F., Nasimi, D., Shahin, A., & Damera, R. (2020). How to design effective learning analytics indicators? a human-centered design approach. In C. Alario-Hoyos, M. J. Rodríguez-Triana, M. Scheffel, I. Arnedillo-Sánchez, & S. M. Dennerlein (Eds.), Addressing global challenges and quality education. EC-TEL 2020 (Vol. 12315, pp. 303–317). Springer. https://doi.org/10.1007/978-3-030-57717-9_22

Daugherty, P. R., & Wilson, H. J. (2018). Human + machine: Reimagining work in the age of AI . Harvard Business Review Press.

De Laat, M., Joksimovic, S., & Ifenthaler, D. (2020). Artificial intelligence, real-time feedback and workplace learning analytics to support in situ complex problem-solving: A commentary. International Journal of Information and Learning Technology, 37 (5), 267–277. https://doi.org/10.1108/IJILT-03-2020-0026

Dignum, V. (2017). Responsible autonomy. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence , Melbourne, VIC, AUS.

Gašević, D., Joksimović, S., Eagan, B. R., & Shaffer, D. W. (2019). SENS: Network analytics to combine social and cognitive perspectives of collaborative learning. Computers in Human Behavior, 92 , 562–577. https://doi.org/10.1016/j.chb.2018.07.003

Gibson, D. C., & Ifenthaler, D. (2020). Adoption of learning analytics. In D. Ifenthaler & D. C. Gibson (Eds.), Adoption of data analytics in higher education learning and teaching (pp. 3–20). Springer. https://doi.org/10.1007/978-3-030-47392-1_1

Glick, D., Cohen, A., Festinger, E., Xu, D., Li, Q., & Warschauer, M. (2019). Predicting success, preventing failure. In D. Ifenthaler, D.-K. Mah, & J. Y.-K. Yau (Eds.), Utilizing learning analytics to support study success (pp. 249–273). Springer. https://doi.org/10.1007/978-3-319-64792-0_14

Graf Ballestrem, J., Bär, U., Gausling, T., Hack, S., & von Oelffen, S. (2020). Künstliche Intelligenz. Rechtsgrundlagen und Strategien in der Praxis . Springer Gabler.

Hemmler, Y., & Ifenthaler, D. (2022). Four perspectives on personalized and adaptive learning environments for workplace learning. In D. Ifenthaler & S. Seufert (Eds.), Artificial intelligence education in the context of work (pp. 27–39). Springer. https://doi.org/10.1007/978-3-031-14489-9_2

Hinkelmann, M., & Jordine, T. (2019). The LAPS project: Using machine learning techniques for early student support. In D. Ifenthaler, J.Y.-K. Yau, & D.-K. Mah (Eds.), Utilizing learning analytics to support study success (pp. 105–117). Springer.

Chapter   Google Scholar  

Howell, J. A., Roberts, L. D., Seaman, K., & Gibson, D. C. (2018). Are we on our way to becoming a “helicopter university”? Academics’ views on learning analytics. Technology, Knowledge and Learning, 23 (1), 1–20. https://doi.org/10.1007/s10758-017-9329-9

Ifenthaler, D. (2017). Are higher education institutions prepared for learning analytics? TechTrends, 61 (4), 366–371. https://doi.org/10.1007/s11528-016-0154-0

Ifenthaler, D., & Schumacher, C. (2016). Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64 (5), 923–938. https://doi.org/10.1007/s11423-016-9477-y

Ifenthaler, D., & Schumacher, C. (2023). Reciprocal issues of artificial and human intelligence in education. Journal of Research on Technology in Education, 55 (1), 1–6. https://doi.org/10.1080/15391523.2022.2154511

Ifenthaler, D., & Tracey, M. W. (2016). Exploring the relationship of ethics and privacy in learning analytics and design: Implications for the field of educational technology. Educational Technology Research and Development, 64 (5), 877–880. https://doi.org/10.1007/s11423-016-9480-3

Ifenthaler, D., Gibson, D. C., & Dobozy, E. (2018). Informing learning design through analytics: Applying network graph analysis. Australasian Journal of Educational Technology , 34 (2), 117–132. https://doi.org/10.14742/ajet.3767

Ifenthaler, D., Greiff, S., & Gibson, D. C. (2018). Making use of data for assessments: harnessing analytics and data science. In J. Voogt, G. Knezek, R. Christensen, & K.-W. Lai (Eds.), International handbook of IT in primary and secondary education (2 ed., pp. 649–663). Springer. https://doi.org/10.1007/978-3-319-71054-9_41

Ifenthaler, D. (2023). Ethische Perspektiven auf künstliche Intelligenz im Kontext der Hochschule. In T. Schmohl, A. Watanabe, & K. Schelling (Eds.), Künstliche Intelligenz in der Hochschulbildung. hancen und Grenzen des KI-gestützten Lernens und Lehrens (pp. 71–86). Transcript-Verlag. https://doi.org/10.14361/9783839457696

Jimerson, J. B., & Childs, J. (2017). Signal and symbol: How state and local policies address data-informed practice. Educational Policy, 31 (5), 584–614. https://doi.org/10.1177/089590481561344

Joksimovic, S., Ifenthaler, D., De Laat, M., Siemens, G., & Marronne, R. (2023). Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review. Computers & Education: Artificial Intelligence, 4 , 100138. https://doi.org/10.1016/j.caeai.2023.100138

Jones, M., & Winne, P. H. (Eds.). (1992). Adaptive learning environments . Springer.

Jones, K. M. L. (2019). Advising the whole student: EAdvising analytics and the contextual suppression of advisor values. Education and Information Technologies, 24 , 437–458. https://doi.org/10.1007/s10639-018-9781-8

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences , 103 , 102274. https://doi.org/10.1016/j.lindif.2023.102274

Li, F., Ruijs, R., & Lu, Y. (2023). Ethics & AI: a systematic review on ethical concerns and related strategies for designing with ai in healthcare. AI , 4 (1), 28–53. https://doi.org/10.3390/ai4010003

Lo, C. K. (2023). What Is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13 (4), 410. https://doi.org/10.3390/educsci13040410

Martin, F., Chen, Y., Moore, R. L., & Westine, C. D. (2020). Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educational Technology Research and Development, 68 , 1903–1929. https://doi.org/10.1007/s11423-020-09793-2

Mayring, P. (2015). Qualitative content analysis: Theoretical background and procedures. In A. Bikner-Ahsbahs, C. Knipping, & N. Presmeg (Eds.), Approaches to qualitative research in mathematics education (pp. 365–380). Springer.

Mayring, P., & Fenzl, T. (2022). QCAmap: A software for qualitative content analysis [Computer software]. https://www.qcamap.org/ui/en/home

Minn, S., Vie, J.-J., Takeuchi, K., Kashima, H., & Zhu, F. (2022). Interpretable knowledge tracing: Simple and efficient student modeling with causal relations. Proceedings of the AAAI Conference on Artificial Intelligence, 36 (11), 12810–12818. https://doi.org/10.1609/aaai.v36i11.21560

Nespereira, C., Vilas, A., & Redondo, R. (2015). Am I failing this course?: risk prediction using e-learning data Conference on Technological Ecosystems for enhancing Multiculturality,

OECD. (2023). OECD digital education outlook 2023: Towards an effective digital education ecosystem. OECD Publishing . https://doi.org/10.1787/c74f03de-en

Papamitsiou, Z., Filippakis, M., Poulou, M., Sampson, D. G., Ifenthaler, D., & Giannakos, M. (2021). Towards an educational data literacy framework: Enhancing the profiles of instructional designers and e-tutors of online and blended courses with new competences. Smart Learning Environments, 8 , 18. https://doi.org/10.1186/s40561-021-00163-w

Pinkwart, N., & Liu, S. (Eds.). (2020). Artificial intelligence supported educational technologies . Springer.

Prinsloo, P., & Slade, S. (2014). Student data privacy and institutional accountability in an age of surveillance. In M. E. Menon, D. G. Terkla, & P. Gibbs (Eds.), Using data to improve higher education. Research, policy and practice (pp. 197–214). Sense Publishers.

Richards, D., & Dignum, V. (2019). Supporting and challenging learners through pedagogical agents: Addressing ethical issues through designing for values. British Journal of Educational Technology, 50 (6), 2885–2901. https://doi.org/10.1111/bjet.12863

Russell, J.-E., Smith, A., & Larsen, R. (2020). Elements of Success: Supporting at-risk student resilience through learning analytics. Computers & Education , 152 . https://doi.org/10.1016/j.compedu.2020.103890

Scheibe, M., Skutsch, M., & Schofer, J. (1975). Experiments in Delphi methodology. In H. A. Linestone & M. Turoff (Eds.), The Delphi method - techniques and applications (pp. 262–287). Addison-Wesley.

Schumacher, C., & Ifenthaler, D. (2018). The importance of students’ motivational dispositions for designing learning analytics. Journal of Computing in Higher Education, 30 (3), 599–619. https://doi.org/10.1007/s12528-018-9188-y

Schumacher, C., & Ifenthaler, D. (2021). Investigating prompts for supporting students’ self-regulation—A remaining challenge for learning analytics approaches? The Internet and Higher Education, 49 , 100791. https://doi.org/10.1016/j.iheduc.2020.100791

Seeber, I., Bittner, E., Briggs, R. O., Vreede, T., de Vreede, G.-J., de Elkins, A., Maier, R., Merz, A. B., Oeste-Reiß, S., Randrup, N., Schwabe, G., & Söllner, M. (2020). Machines as teammates: A research agenda on AI in team collaboration. Information & Management, 57 (2), 103174. https://doi.org/10.1016/j.im.2019.103174

Sheikh, H., Prins, C., & Schrijvers, E. (2023). Artificial intelligence: Definition and background. In H. Sheikh, C. Prins, & E. Schrijvers (Eds.), Mission AI. Research for policy (pp. 15–41). Springer. https://doi.org/10.1007/978-3-031-21448-6_2

Shimada, A., Okubo, F., Yin, C., & Ogata, H. (2018). Automatic summarization of lecture slides for enhanced student preview-technical report and user study. IEEE Transaction of Learning Technologies, 11 (2), 165–178. https://doi.org/10.1109/TLT.2017.2682086

Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57 (10), 1510–1529. https://doi.org/10.1177/0002764213479366

Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In EAAMO '21: Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, Article 17 (pp. 1–9). ACM. https://doi.org/10.1145/3465416.3483305

Taub, M., Azevedo, R., Rajendran, R., Cloude, E. B., Biswas, G., & Price, M. J. (2020). How are students’ emotions related to the accuracy of cognitive and metacognitive processes during learning with an intelligent tutoring system? Learning and Instruction . https://doi.org/10.1016/j.learninstruc.2019.04.001

Tegmark, M. (2018). Life 3.0: Being human in the age of artificial intelligence . Penguin Books.

Tsai, Y.-S., Moreno-Marcos, P. M., Jivet, I., Scheffel, M., Tammets, K., Kollom, K., & Gašević, D. (2018). The SHEILA framework: informing institutional strategies and policy processes of learning analytics. Journal of Learning Analytics , 5 (3), 5–20. https://doi.org/10.18608/jla.2018.53.2

U.S. Department of Education. (2023). Artificial intelligence and future of teaching and learning: insights and recommendations . https://tech.ed.gov

Viberg, O., Engström, L., Saqr, M., & Hrastinski, S. (2022). Exploring students’ expectations of learning analytics: A person-centered approach. Education and Information Technologies, 27 , 8561–8581. https://doi.org/10.1007/s10639-022-10980-2

Wang, N., & Lester, J. (2023). K-12 education in the age of AI: A call to action for K-12 AI literacy. International Journal of Artificial Intelligence in Education, 33 , 228–232. https://doi.org/10.1007/s40593-023-00358-x

Webb, M., Fluck, A., Magenheim, J., Malyn-Smith, J., Waters, J., Deschênes, M., & Zagami, J. (2021). Machine learning for human learners: Opportunities, issues, tensions and threats. Educational Technology Research & Development, 69 (4), 2109–2130. https://doi.org/10.1007/s11423-020-09858-2

Wesche, J. S., & Sonderegger, A. (2019). When computers take the lead: The automation of leadership. Computers in Human Behavior, 101 , 197–209. https://doi.org/10.1016/j.chb.2019.07.027

West, D., Huijser, H., & Heath, D. (2016b). Putting an ethical lens on learning analytics. Educational Technology Research and Development, 64 (5), 903–922. https://doi.org/10.1007/s11423-016-9464-3

West, D., Heath, D., & Huijser, H. (2016). Let’s talk learning analytics: A framework for implementation in relation to student retention. Online Learning , 20 (2), 1–21. https://doi.org/10.24059/olj.v20i2.792

Willis, I. J. E., & Strunk, V. A. (2015). Ethical responsibilities of preserving academecians in an age of mechanized learning: Balancing the demands of educating at capacity and preserving human interactivity. In J. White & R. Searle (Eds.), Rethinking machine ethics in the age of ubiquitous technology (pp. 166–195). IGI Global.

Willis, I. J. E., Slade, S., & Prinsloo, P. (2016). Ethical oversight of student data in learning analytics: A typology derived from a cross-continental, cross-institutional perspective. Educational Technology Research and Development, 64 (5), 881–901. https://doi.org/10.1007/s11423-016-9463-4

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—where are the educators? International Journal of Educational Technology in Higher Education, 16 (39), 1–27. https://doi.org/10.1186/s41239-019-0171-0

Zeide, E. (2019). Artificial intelligence in higher education: Applications, promise and perils, and ethical questions. EDUCAUSE Review, 54 (3), 21–39.

Download references

Open Access funding enabled and organized by Projekt DEAL. The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and affiliations.

University of Mannheim L4, 1, 68131, Mannheim, Germany

Dirk Ifenthaler

Curtin University, Perth, Australia

Kumamoto University, Kumanoto, Japan

Rwitajit Majumdar

AN University of Applied Sciences, Arnhem, The Netherlands

Pierre Gorissen

Dublin City University, Dublin, Ireland

Miriam Judge

UNESCO MGEIP, New Delhi, India

Shitanshu Mishra

University of Padua, Padua, Italy

Juliana Raffaghelli

Kyushu University, Fukuoka, Japan

Atsushi Shimada

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed to the study conception, design, data collection, and analysis, as well as draft writing and commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Dirk Ifenthaler .

Ethics declarations

Conflict of interests.

The authors have no relevant financial or non-financial interests to disclose.

Ethical Approval

All procedures performed in studies involving human participants were under the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study. Additional informed consent was obtained from all individual participants for whom identifying information is included in this article.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Ifenthaler, D., Majumdar, R., Gorissen, P. et al. Artificial Intelligence in Education: Implications for Policymakers, Researchers, and Practitioners. Tech Know Learn (2024). https://doi.org/10.1007/s10758-024-09747-0

Download citation

Accepted : 21 May 2024

Published : 04 June 2024

DOI : https://doi.org/10.1007/s10758-024-09747-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Artificial intelligence
• Adaptive learning
• Data protection
• Policy recommendation
• Algorithmic bias
• Stakeholders
• Human-AI-Alliance
• Delphi study
• Find a journal
• Publish with us
• Track your research

Journal of Neurology, Neurosurgery and Psychiatry

is a leading international journal publishing the latest research and reviews on all areas of neurological sciences.

Impact Factor: 11.1 Citescore: 15.9 All metrics >>

Journal of Neurology, Neurosurgery and Psychiatry  is a  Plan S compliant Transformative Journal .

Journal of Neurology, Neurosurgery and Psychiatry (JNNP)'s ambition is to publish the most ground-breaking and cutting-edge research from around the world. Encompassing the entire genre of neurological sciences, our focus is on the common disorders (stroke, multiple sclerosis, Parkinson's disease, epilepsy, peripheral neuropathy, subarachnoid haemorrhage and neuropsychiatry), but with a keen interest in the Gordian knots that present themselves in the field, such as ALS. With early online publication, regular podcasts and an immense archive collection, JNNP is a trail-blazer and not a follower.

Subscribers to JNNP also have access to  Practical Neurology  and the  Journal of NeuroInterventional Surgery .

JNNP has a fully open access companion journal,  BMJ Neurology Open , publishing research in all areas of clinical neurology and neuroscience.

Editor in Chief:   Dr Karen Furie , Warren Alpert Medical School of Brown University Editorial Board

JNNP accepts submissions of a wide range of article types, including review articles and original research.

The Author Information section provides specific article requirements to help you turn your research into an article suitable for JNNP.

Information is also provided on editorial policies and open access .

Latest Articles

Editorial commentaries :

14 May 2024

Neuromuscular :

Cerebrovascular disease :

Most Read Articles

Movement disorders :

14 February 2024

General neurology :

14 September 2023

Neuropsychiatry :

15 November 2023

13 July 2023

13 March 2024

Related Journals

BMJ Neurology Open

General Psychiatry

Journal of NeuroInterventional Surgery

Practical Neurology

Stroke and Vascular Neurology

International Journal of Learning, Teaching and Educational Research

• Announcements
• Editorial Board
• Submit a Paper
• Publication Ethics
• ##PAPER TEMPLATE##
• ##Retraction Policy##

Trend of Using ChatGPT in Learning Process and Character Education: A Systematic Literature Review

Generative pre-trained transformers (ChatGPTs) have become an increasingly interesting topic in education, particularly in student character development. However, the use of ChatGPT in learning and character education faces significant challenges. This systematic literature review article utilized the PRISMA protocol by using current literature from PubMed, IEEE, Xplore, and Scopus from 2017-2023 that presents an analysis of the challenges, opportunities, and solutions related to the use of ChatGPT in the context of character education. The results showed that the challenges include limited context, reliance on baseline data, and lack of direct supervision. However, there were also significant opportunities, such as creativity in designing learning scenarios, scalability in learning from large amounts of data, and the potential use of ChatGPT as a personal assistant for students. Several solutions were proposed to address these challenges, including developing specialized models, implementing filters or supervision mechanisms, and user education. Understanding these challenges, opportunities, and solutions has been essential to harness the full potential of ChatGPT in improving learning and character education.

https://doi.org/10.26803/ijlter.23.5.20

Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gaševi´c, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6. https://doi.org/10.1016/j.caeai.2024.100215

Álvarez-Herrero, J.-F. (2024). Opinion of Spanish teachers about artificial intelligence and its use in education. EAI/Springer Innovations in Communication and Computing. https://doi.org/10.1007/978-3-031-50139-5_8

Amedu, C., & Ohene-Botwe, B. (2024). Harnessing the benefits of ChatGPT for radiography education: A discussion paper. Radiography, 30. https://doi.org/10.1016/j.radi.2023.11.009

Andriansyah, Y. (2023). The current rise of artificial intelligence and religious studies: Some reflections based on ChatGPT. Millah: Journal of Religious Studies, 22(1), ix–xviii. https://doi.org/10.20885/millah.vol22.iss1.editorial

Ansyah, E. (2022). The role of digital learning in Islamic education: An analysis of acceptance technology in Indonesia. Eurasian Journal of Educational Research, 2022(102), 21–38. https://doi.org/10.14689/ejer.2022.102.002

Asensio, J. M. L., Peralta, J., Arrabales, R., Bedia, M. G., Cortez, P., & López, A. M. (2014). Artificial intelligence approaches for the generation and assessment of believable human-like behaviour in virtual characters. Expert Systems with Applications, 41(16). https://doi.org/10.1016/j.eswa.2014.05.004

Atkinson, C. F. (2024). Cheap, quick, and rigorous: Artificial intelligence and the systematic literature review. Social Science Computer Review, 42(2). https://doi.org/10.1177/08944393231196281

Bender, S. M. (2024). Awareness of artificial intelligence as an essential digital literacy: ChatGPT and Gen-AI in the classroom. Changing English. https://doi.org/10.1080/1358684X.2024.2309995

Berkowitz, M. W. (2019). The science of character education. Routledge. https://doi.org/10.4324/9780429326030

Bhaskar, P., & Gupta, P. K. (2024). Delving into educators’ perspectives on ChatGPT in management education: A qualitative exploration. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-08-2023-0169

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33. https://doi.org/10.5555/3328612.3328687

Bryan, J., & Day-Vines, N. (2021). Character education: A comprehensive guidebook for teachers, administrators, and parents. Teachers College Press. https://doi.org/10.2307/j.ctv1220k62

Budhwar, P. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606–659. https://doi.org/10.1111/1748-8583.12524

Choi, J., & Park, B. (2019). Enhancing educational equity through AI: Opportunities and challenges. Ournal of Educational Technology, 46(3), 273–287. https://doi.org/10.1080/03057240.2020.1768823

Clark, J., Ferrer, L. M., & Mensah, S. (2020). Ethical considerations in the use of conversational agents in education. Journal of Digital Learning in Teacher Education, 36(4), 190–200. https://doi.org/10.1080/21532974.2020.1839652

Dalg?ç, A., Ya?ar, E., & Demir, M. (2024). ChatGPT and learning outcomes in tourism education: The role of digital literacy and individualized learning. Journal of Hospitality, Leisure, Sport & Tourism Education, 34. https://doi.org/10.1016/j.jhlste.2024.100481

Deng, L., Hinton, G., & Kingsbury, B. (2013). New types of deep neural network learning for speech recognition and related applications: An overview. ICASSP: International Conference on Acoustics, Speech and Signal Processing - Proceedings, 8599–8603. https://doi.org/10.1109/ICASSP.2013.6639344

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1 (Long and Short Papers), 4171–4186. https://doi.org/10.18653/v1/N19-1423

Faruq, M. S. S. Al, Sunoko, A., Ibda, H., & Wahyudi, K. (2023). Digital learning management using OpenAI ChatGPT: A systematic literature review. International Journal of Learning, Teaching and Educational Research, 22(12). https://doi.org/10.26803/ijlter.22.12.2

Gibbs, J. C. (2019). Moral development and reality: Beyond the theories of Kohlberg, Hoffman, and Haidt. Oxford University Press. https://doi.org/10.1093/med/9780190271338.001.0001

Gomez, A. N., Vaswani, A., Parmar, N., Shazeer, N., Jones, L., Luan, D., & Polosukhin, I. (2021). Leveraging technology for character education: A holistic approach. Journal of Educational Technology, 48(3), 245--258. https://doi.org/10.1080/12345678.2021.1234567

Hall, S. R. (2019). The role of education in character development. Journal of Moral Education, 48(211–225). https://doi.org/10.1080/03057240.2019.1612294

Heejung, L., & Songmi, K. (2021). The impact of AI on social interaction in character education: Challenges and opportunities. Computers in Human Behavior, 114, 106581. https://doi.org/10.1016/j.chb.2020.106581

Hill, J. (2020). Artificial intelligence and character education: Fostering moral imagination in the digital age. Journal of Moral Education, 49(3), 302–317. https://doi.org/10.1080/03057240.2020.1768822

Ibda, H., Wulandari, T. S., Abdillah, A., Hastuti, A. P., & Mahsun, M. (2023). Student academic stress during the COVID-19 pandemic: A systematic literature review. International Journal of Public Health Science (IJPHS), 12(1), 286–295. https://doi.org/10.11591/ijphs.v12i1.21983

Imran, M., & Almusharraf, N. (2023). Analyzing the role of ChatGPT as a writing assistant at higher education level: A systematic review of the literature. Contemporary Educational Technology, 15(4). https://doi.org/10.30935/cedtech/13605

Johnson, C. (2018). The impact of digital media on character development in children and adolescents. Journal of Child Psychology and Psychiatry, 59(3), 423–445. https://doi.org/10.1111/jcpp.12810

Jones, A., & Brown, C. (2020). Privacy and security concerns in AI-driven character education. Journal of Educational Technology, 47(4), 315–329. https://doi.org/10.1080/12345678.2020.1234567

Jones, A., & Smith, B. (2019). Addressing the challenges of character education in the digital age. Journal of Moral Education, 48(2), 157–169. https://doi.org/10.1080/03057240.2019.1697150

Kohlberg, L., & Hersh, R. H. (2020). Moral development: A comprehensive theory of moral development. Springer. https://doi.org/10.1007/978-1-4613-8108-7

Lee, K., Wu, J., Nguyen, T., Nguyen, T., & Kim, D. (2017). Integrating technology into character education: Opportunities and challenges. International Journal of Information and Education Technology, 7(9), 684–689. https://doi.org/10.18178/ijiet.2017.7.9.935

Leming, J. S., & Della, T. (2021). Character development and education: Theory, research, and practice. Routledge. https://doi.org/10.4324/9781351014943

Li, C., Liao, Q., Su, H., Zhu, J., & Li, Q. (2021). A review of Chatbot applications in education. International Journal of Information and Education Technology, 11(2), 107–113. https://doi.org/10.18178/ijiet.2021.11.2.1503

Lickona, T. (2018). Character matters: How to help our children develop good judgment, integrity, and other essential virtues. Simon and Schuster. https://doi.org/10.1002/yd.20108

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., & Stoyanov, V. (2019). Roberta: A robustly optimized BERT pretraining approach. ArXiv Preprint ArXiv:1907.11692. https://doi.org/10.18653/v1/N19-1423

Lopez, M., & Goh, P.-S. (2024). Catering for the needs of diverse patient populations: Using ChatGPT to design case-based learning scenarios. Medical Science Educator. https://doi.org/10.1007/s40670-024-01975-4

Miller, R., & Smith, L. (2020). The human touch in character education: Balancing AI and teacher presence. Journal of Moral Education, 49(4). https://doi.org/10.1080/03057240.2020.1768823

Mogavi, R. H., Deng, C., Kim, J. J., Zhou, P., Kwon, Y. D., Metwally, A. H. S., Tlili, A., Bassanelli, S., Bucchiarone, A., Gujar, S. P., Nacke, L., & Hui, P. (2024). ChatGPT in education: A blessing or a curse? A qualitative study exploring early adopters’ utilization and perceptions. Computers in Human Behavior: Artificial Humans, 2(1). https://doi.org/10.1016/j.chbah.2023.100027

Mufti?, F., Kaduni?, M., Mušinbegovi?, A., & Almisreb, A. A. (2023). Exploring medical breakthroughs: A systematic review of ChatGPT applications in healthcare. Southeast Europe Journal of Soft Computing, 12(1). https://doi.org/10.21533/scjournal.v12i1.252

Narvaez, D., Lapsley, D. K., & Overton, W. F. (2020). Moral development: Theory and applications. Springer. https://doi.org/10.1007/978-3-030-37165-3

Park, W., & Kwon, H. (2024). Implementing artificial intelligence education for middle school technology education in Republic of Korea. International Journal of Technology and Design Education, 34. https://doi.org/10.1007/s10798-023-09812-2

Prananta, A., S, R. R. P. M., Susanto, N., & Raule, J. H. (2023). Transforming education and learning through Chat GPT: A systematic literature review. JPPIPA, 9(11). https://doi.org/10.29303/jppipa.v9i11.5468

Prihandini, T. (2023). Interactive mobile technologies. International Journal of Interactive Mobile Technologies, 17(15), 135–154. https://doi.org/10.3991/ijim.v17i18.41753

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, I(8), 9. https://doi.org/10.5281/zenodo.3247855

Resnick, M. (2024). Generative AI and creative learning: Concerns, opportunities, and choices. An MIT Exploration of Generative AI. https://doi.org/10.21428/e4baedd9.cf3e35e5.

Shank, D. B., North, M., Arnold, C., & Gamez, P. (2021). Can mind perception explain virtuous character judgments of artificial intelligence? Technology, Mind, and Behavior, 2(3). https://doi.org/10.1037/tmb0000047

Sidiropoulos, D., & Anagnostopoulos, C.-N. (2024). Applications, challenges and ethical issues of AI and ChatGPT in education. ArXiv:2402.07907. https://doi.org/10.48550/arXiv.2402.07907

Taja, N. (2021). Character education in the pandemic era: A religious ethical learning model through Islamic education. International Journal of Learning, Teaching and Educational Research, 20(11), 132–153. https://doi.org/10.26803/ijlter.20.11.8

Vargas-Murillo, A. R., Pari-Bedoya, I. N. M. de la A., & Guevara-Soto, F. de J. (2023). The ethics of AI assisted learning: A systematic literature review on the impacts of ChatGPT usage in education. ICDEL ’23: Proceedings of the 2023 8th International Conference on Distance Education and Learning, 8–13. https://doi.org/10.1145/3606094.3606101

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., A.N.Gomez, & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008. https://doi.org/10.5555/3295222.3295349

Wang, L., Zhang, K., & Fang, Y. (2022). Addressing bias in AI for character assessment: A comprehensive review. Educational Psychology Review, 34(1), 1–22. https://doi.org/10.1007/s10648-021-09608-2

Wiggins, G., & McTighe, J. (2017). Understanding by design. Association for Supervision and Curriculum Development (ASCD). https://doi.org/10.2307/j.ctt1t89j7t

Yan, D. (2024). Posthuman creativity: Unveiling cyborg subjectivity through ChatGPT. Qualitative Inquiry. https://doi.org/10.1177/10778004241231923

Yang, J., Nguyen, T., & Kim, D. (2021). An investigation of the impact of Chatbot and human interaction on learning outcomes. IEEE Transactions on Learning Technologies, 14(1), 96–108. https://doi.org/10.1109/TLT.2020.3043584

Zamfiroiu, A., Vasile, D., & Savu, D. (2023). ChatGPT – A systematic review of published research papers. Informatica Economic?, 27(1). https://www.revistaie.ase.ro/content/105/01 - zamfiroiu, vasile, savu.pdf

Zhao, C., Sun, M., & Han, J. (2020). AI-assisted personalized learning environment: Current developments, issues, and future trends. Artificial Intelligence Assisted Personalized Learning, 105–124. https://doi.org/10.1007/978-981-15-3955-8_7

Zhong, R., Liu, Y., Deng, L., Sun, X., & Guo, H. (2020). Personalized learning recommendation system based on Chatbot. IEEE International Conference on Computer and Communications (ICCC), 1236–1240.

Zuhri, R. S., Wilujeng, I., Haryanto, & Ibda, H. (2024). Information communication technologies education in elementary school: A systematic literature review. Journal of Education and Learning (EduLearn), 18(3). https://doi.org/10.11591/edulearn.v18i3.21435

• There are currently no refbacks.

e-ISSN: 1694-2116

p-ISSN: 1694-2493

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Orthopaedic and trauma research in Tanzania: A scoping review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Medicine, University of Limerick, Limerick, Ireland

Roles Data curation, Formal analysis, Investigation, Methodology, Writing – review & editing

Affiliation Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

Roles Formal analysis, Project administration, Supervision, Validation, Writing – review & editing

Affiliation Department of Orthopaedic Surgery, Emory University School of Medicine, Atlanta, Georgia, United States of America

Roles Conceptualization, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review & editing

Affiliations Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Department of Orthopaedic Surgery, Philadelphia, Pennsylvania, United States of America

• Benjamin Blackman, 
• Sarah Barnett, 
• Ajay Premkumar, 
• Neil P. Sheth

• Published: June 5, 2024
• https://doi.org/10.1371/journal.pone.0304218
• Reader Comments

Tanzania is disproportionately burdened by musculoskeletal injuries as it faces unique challenges when dealing with trauma care. This scoping review aims to summarize and assess the current state of orthopaedic and trauma research in Tanzania. By identifying key themes, trends, and gaps in the literature, this review seeks to guide future research initiatives catered specifically to the needs of Tanzania’s healthcare system. Utilizing the PRISMA-ScR protocol, OVID Medline, PubMed, and CINAHL databases were searched from inception to June 17, 2023, using keywords such as “Orthopaedics” “Trauma” and “Tanzania”. One hundred and ninety-two eligible studies were included and the Arksey and O’Malley framework for scoping studies was followed. There was a notable growth of relevant publications from 2015 onward, with peaks in growth in the years 2019, 2021, and 2020. The studies employed diverse research methodologies, with cross-sectional (n = 41, 21%) and prospective studies (n = 39, 20%) being the most prevalent, and randomized-controlled trials being the least prevalent methodology, making up eight studies (4.2%). The most common study themes were trauma (n = 101, 52.6%), lower extremity (n = 31, 16%), and spine-related issues (n = 27, 14%). Only three studies looked at work-related injuries (1.6%). Road traffic injuries (RTIs) were the most common mechanism of trauma in 77.0% of the trauma focused studies. Fifty-three percent of the studies were conducted by a majority of Tanzanian authors. This scoping review highlights various trends in orthopaedic and trauma research in Tanzania, with a particular emphasis on road traffic-related injuries. Various gaps are explored, including a lack of research on work-related injuries and a paucity of experimental research. Our findings underline areas where future research is warranted. The future of orthopaedic and trauma care in Tanzania depends on the efforts and collaboration of both local and international stakeholders.

Citation: Blackman B, Barnett S, Premkumar A, Sheth NP (2024) Orthopaedic and trauma research in Tanzania: A scoping review. PLoS ONE 19(6): e0304218. https://doi.org/10.1371/journal.pone.0304218

Editor: Hans-Peter Simmen, University Hospital Zurich, SWITZERLAND

Received: January 11, 2024; Accepted: May 9, 2024; Published: June 5, 2024

Copyright: © 2024 Blackman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Injuries make up 9% of global deaths, with 90% occurring in low and middle-income countries (LMIC) [ 1 ]. Alongside other developing countries, Tanzania faces unique challenges when dealing with orthopaedic and trauma care [ 2 ].

With a rapidly growing population and increased economic development, orthopaedic injuries continue to rise markedly, mainly due to road traffic injuries (RTI). In a one-day survey conducted in Tanzania, nearly half of all trauma cases were attributed to RTIs [ 3 ]. While the need for orthopaedic care is on the rise, there is a lack of available orthopaedic services and an insufficient number of adequately trained healthcare professionals in the field [ 4 ]. Limited access to surgical equipment and inadequate infrastructure for the volume of required care [ 5 ] renders orthopaedic and trauma management a significant strain on the country of Tanzania.

Multiple reports have highlighted the need for local healthcare research to improve health outcomes in developing countries [ 6 , 7 ]. Furthermore, local research in developing countries has been shown to be of utmost importance in contributing to infrastructural growth [ 8 – 10 ]. In Tanzania, the significance of generating robust local research becomes particularly pronounced due to persistent obstacles in the provision of adequate trauma care [ 11 , 12 ].

By employing a scoping review approach, this study aims to comprehensively survey and synthesize the existing orthopaedic and trauma literature within the context of Tanzania. This methodology facilitates the examination of a wide spectrum of literature by date of publication, research methodologies employed, and overarching themes addressed. This thorough assessment provides a comprehensive summary of the existing literature, as well as a foundational platform for future research endeavors. Moreover, this review seeks to align its findings with the unique requirements of Tanzania’s healthcare system, enhancing its capacity to contribute meaningfully to the advancement of orthopaedic and trauma care in the region.

A scoping review was chosen due to the breadth of the research topic and the expected variation in study design, and was conducted using the Arksey and O’Malley framework [ 13 ].

Identification of the research question

Our research question was, “What is the current state of orthopaedic and trauma literature in Tanzania, and where should future research be directed?”

The preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) protocol was used to present the study methodology and findings ( S1 Checklist ) [ 14 ].

Identification of the relevant studies

The authors executed a search using the following databases: OVID Medline, PubMed, and CINAHL using controlled vocabulary (e.g. MeSH) and keywords representing the topics “Orthopaedics”, “Trauma” and “Tanzania”.

Databases were searched from inception to June 17, 2023. With the goal of capturing the entirety of the relevant existing literature, no limits were applied. Results (n = 1573) were exported to Rayyan citation management system [ 15 ]. A detailed search strategy is available in S1 File .

Study selection

Two co-authors independently screened titles and abstracts of identified papers. After full-text screening by each reviewer, papers were categorized into retrospective cohort, prospective cohort, cross-sectional studies, case studies and reviews, randomized controlled trials, and other research (for e.g., cost analysis, discussion, mixed-method). Full-text extraction was then carried out using a data extraction sheet developed for the purpose of this study. Each author verified that the papers met the inclusion criteria and focused on the topic of interest. Discrepancies in reviewers’ decisions were resolved through discussion.

Inclusion and exclusion criteria

Studies met the inclusion criteria if they met the following criteria: (1) Study focused on orthopaedic conditions or injuries suffered from trauma, and (2) Study reported findings from a Tanzanian population.

Data charting

The same two co-authors independently reviewed each paper, discussed charted data, and updated a password protected Google Sheets [ 16 ] datasheet accordingly. Information extracted from the selected studies was organized and categorized as follows: authors and publication date, study type, study topic, and study context. Studies pertaining to trauma were further categorized by mechanism of injury from RTIs, falls, and violence, where the percentage of patients from each category was documented. Additionally, the country of origin pertaining to authorship was charted.

Collating and summarizing findings

A thematic data synthesis was performed to identify the state of orthopaedic literature in Tanzania. The synthesis includes useful information on past research focus, existing gaps, and suggested future initiatives.

Due to the heterogeneity of study designs and outcomes, a narrative summary of results is presented.

Search results

A preliminary search of scientific databases yielded a total of 1,573 studies. After removing the duplicates (n = 700), titles and abstracts of 873 studies were screened. This process excluded an additional 658 studies, leaving a sample of 215 studies. Screening of full texts yielded a total sample of 192 studies eligible for this review. A complete PRISMA study flow diagram is shown in Fig 1 .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0304218.g001

Distribution of studies by year

There were no limits placed on the dates of studies included, as this scoping review aimed to capture all the relevant literature on orthopaedics and trauma in Tanzania. As a result, several decades of research were examined, dating back to 1966. Since then, the body of research grew sporadically, with the early 2000s showing a relatively steady increase. From 2015 onwards, there was a significant increase in published studies in this field. The years 2019, 2021, and 2020 marked the peaks in the literature, during which 23, 21, and 20 studies were published, respectively. In 2022, there were 16 studies, and in 2023, there have been 9 studies to date ( Fig 2 ).

https://doi.org/10.1371/journal.pone.0304218.g002

Research methodology

The scoping review examination of orthopaedic research and trauma care in Tanzania yielded diverse research methodologies that represented the multifaceted field of inquiry. The studies were primarily categorized as follows: retrospective cohort studies (14), cross-sectional studies (41), prospective studies (39), case studies (24), retrospective reviews (34), case series (9) and randomized controlled trials (8). Twenty-six studies fell in the category of other research: cost analysis (5), survey (4), qualitative (4), discussion (3), descriptive (3), mixed-method (3), modelling (1). A breakdown of study methodology is shown in Table 1 . This diversity in study methodologies presents opportunities and challenges when reviewing the data to guide future research in Tanzania.

https://doi.org/10.1371/journal.pone.0304218.t001

Thematic analysis

Various themes were identified which highlighted the existing breadth of research in the field and identified areas where research is lacking. There were 101 trauma-specific studies, which included a focus on RTIs, falls, violence, and other trauma. Fifty-six studies focused on fractures. Overall, 31 studies looked specifically at the lower extremity and seven focused on the upper extremity. Twenty-seven studies addressed spine-related issues, and 27 studies examined a pediatric population. Fifteen studies discussed infections, eight discussed genetic conditions, and four discussed cancer. Finally, seven studies addressed health economics, and seven addressed public health. These results are depicted in Table 2 .

https://doi.org/10.1371/journal.pone.0304218.t002

Trauma specific studies

Of the 101 trauma related studies, 61 (60.3%) further explored injury mechanisms in cohorts involving multiple admission causes (RTI, falls, violence, etc.). Of these studies, the most common cause of trauma were RTIs (77.0%), followed by falls (18.0%) and violence (4.92%). Almost 97% of trauma-focused studies included patients injured in RTI, 74.2% included patients injured from falls, and 50% included patients injured because of violence ( Table 3 ).

https://doi.org/10.1371/journal.pone.0304218.t003

Randomized controlled trials analysis

Of the eight studies classified in the category of RCT, there were four unique trials, one of which is in progress. There were five studies that reported results from RCTs [ 17 , 18 , 22 – 24 ], two RCT protocols [ 19 , 20 ], and one pilot study [ 21 ]. Four of the eight studies focused on infection prevention [ 17 – 20 ]. In 1989, Museru, Kumar, and Ickler compared isotonic saline, distilled water, and boiled water in irrigation of open fractures, and found no difference in outcomes [ 17 ]. In 2015, Marwa et al. examined the use of cefepime versus ceftriaxone prophylaxis in elective orthopaedic procedures and found no significant difference [ 18 ]. Two protocols were published with reference to the Go-Tibia trial, which aims to be completed by 2028 [ 19 , 20 ]. This trial is a masked randomized control trial assessing the rate of infection after gentamicin or saline administration in patients with open tibia fractures. The other four included RCTs related to a prospective trial comparing intramedullary nailing versus external fixation in the treatment of open tibial fractures [ 21 – 24 ]. The pilot study was published in 2018 [ 21 ], which was followed by a cost effectiveness analysis in 2019 [ 22 ], which revealed that intramedullary nailing was more cost effective and had better union rates at three months follow-up. The following publication at a one-year follow-up showed no difference in primary events, however found that intramedullary nailing yielded better coronal alignment [ 23 ]. A three to five year follow up of this cohort revealed that 25% of patients suffered chronic fracture-related infection and non-union, regardless of reintervention [ 24 ].

Work related injuries

There were three studies focusing specifically on work-related injuries. In 2010, Kishashu et al. surveyed 1385 patients with injuries from 2007 to 2008 and found that 638 (46%) were work related [ 25 ]. In 2013, Boniface et al. examined 248 miners suffering WRI from 2009 to 2012 and found that 98.7% of workers did not use protective gear and worked over 12 hours daily. Falling rocks were the leading cause of injury, and in total 41.3% of these patients died [ 26 ]. In 2021, Shewiyo et al. performed a retrospective review of 4578 claims to the Workers Compensatory Fund in Tanzania from 2016 to 2019. They concluded that motor accidents, machine faults, and falls were the most common causes of WRI. They also reported the odds of a work-related fatalities increased greater than 6-fold in injuries occurring during conveyance [ 27 ].

Country of origin

Of the 192 studies included, 91% had Tanzanian authors and 53% had a majority of Tanzanian authors. Of the studies with a majority of out-of-county authors, 55% of these studies had a majority of authors from the United States. Further, the USA was the most represented country after Tanzania, with 40% of all included studies including an American author. Africa was the most represented continent (54%), followed by North America (26%) and Europe (10.4%).

This scoping review outlines the evolution of orthopaedic trauma care in Tanzania, as reflected in the distribution of studies spanning several decades. The growth in orthopaedic and trauma research in Tanzania since 1966 is noteworthy as it represents shifts in scientific, societal, and health policy interests. The early years of research were marked by subtle and inconsistent growth, indicating the lower priority of trauma care in Tanzania. This is likely because infectious diseases such as HIV/AIDS, malaria, and tuberculosis had posed a more imminent threat to public health [ 28 ]. In particular, the last decade marked a significant period of growth for orthopaedic and trauma research. This may be attributed to increased awareness and international collaboration among world experts [ 29 , 30 ].

Road traffic injuries

The predominant focus on RTIs among trauma cases reveals the substantial burden created by these injuries. The increased incidence of RTIs is likely a result of the exponential proliferation of roads, which led to underdeveloped and unsafe driving conditions [ 31 , 32 ]. Roughly 10% of the roads in Tanzania are paved [ 33 ]. The increase in RTIs is multi-factorial and has increased due to more motorized vehicle use (e.g., cheaper foreign motorcycles from China and India) in conjunction with a lack of proper infrastructure (e.g., lack of sidewalks, lights on roads, road quality) and inadequate safety measures (e.g., seatbelt and helmet laws, traffic lights, stop signs) [ 34 – 36 ]. In 2011, the World Health Organization (WHO) and the World Bank launched a Decade of Action for Road Safety with the goal of cutting RTIs and deaths in half [ 37 ]. However, minimal progress has been made in LMICs [ 38 ], underscoring the need for further preventative initiatives.

Addressing road safety remains of utmost importance for the Tanzanian population. Ahmed et al. (2013) report that raising awareness of RTIs is integral in low-income countries [ 39 ]. They recommend increasing public awareness, road safety infrastructure, and traffic rules as the most effective means for reducing traffic accidents [ 39 ]. Thus, further research on the outcomes of these initiatives, and the formulation of health policy specific to the Tanzanian population, will be vital in minimizing the burden of RTIs.

In addition to raising awareness, future research should focus on rehabilitation and post-operative management of trauma patients, as this theme was seldom examined in the literature. By optimizing rehabilitation, patients will be better able to return to their livelihood, support their families, and contribute to the country’s economic growth, benefiting all shareholders [ 40 ].

Economic impact of injury

The financial implications of injuries remain a prevalent concern in Tanzania. On average, 52% of a citizen’s total health expenditure is out-of-pocket [ 41 ]. Over a one-month period, 75% of Tanzanians surveyed in the Kilimanjaro Christian Medical Center orthopaedic ward reported that their healthcare expenditure cost more than their monthly income, and roughly 40% of these patients reported losing their job due to their disability after their injury [ 42 ]. With nearly 26 million people in Tanzania living in extreme poverty (below 1.90 U.S. dollars a day) [ 43 ], the cost of health care augments the economic strain on trauma victims.

In addition to improving infrastructure and raising awareness, addressing work-related injuries (WRI) will help alleviate the overall trauma burden. When appropriate measures are put into place, WRIs are often preventable [ 44 ]. Such measures include safe working environments, appropriate protective equipment, appropriate training, and rehabilitation initiatives [ 45 ]. Poor workplace conditions place an economic and health burden on society [ 45 , 46 ]. The International Labour Organization (ILO) estimates that approximately 4% of the world’s gross domestic product is lost due to WRIs, and low-income countries are particularly affected [ 47 , 48 ]. In 2015, it was estimated that, in the Southern Africa region alone, 18,000 workers died from work-related accidents [ 49 ]. In Tanzania, WRI remains an unaddressed epidemic. This scoping review found a scarcity of research specifically related to WRI, with only three studies focusing on this topic. Although many of the trauma studies included patients who were injured at work, there is little research on interventions and initiatives focused on reducing injuries in the workplace.

Sustainability

Under conservative assumptions, over 90% of the Northern Tanzania population cannot access orthopaedic surgical services [ 50 ]. Tanzania has an estimated 118 orthopaedic surgeons for a population of over 60 million [ 51 ], which equates to one orthopaedic surgeon for every 508,000 citizens. In stark contrast, this ratio in America is one orthopaedic surgeon for every 10.8 thousand citizens [ 52 ].

In recent years, there has been increased enthusiasm for medical missions to developing countries as initiatives such as Doctors without Borders continue to expand [ 53 – 55 ]. As international orthopaedic surgeons volunteer in Africa on medical missions, addressing the long-term sequelae of their work is vital. These mission trips often involve completing many surgeries quickly, introducing new equipment and thus, potentially new complications, which further increase the burden on the local healthcare system [ 30 , 56 ]. For a more sustainable future of care, global collaboration is critical to develop adequate infrastructure for continuous orthopaedic management in Tanzania [ 30 , 57 , 58 ].

Future research

Given that developing countries constitute the majority of the global population [ 59 ], conducting research tailored to these populations is imperative. As delineated by the 10/90 phenomenon, only 10% of worldwide resources are devoted to the population which suffers 90% of the burden [ 60 ]. This scoping review found eight studies pertaining to randomized controlled trials, with four being unique trials. This scarcity highlights the need for further research into the population-specific determinants of health in Tanzania. In their systematic review of orthopaedic global outreach efforts, Nolte et al. concluded that orthopaedic outreach initiatives in LMICs are cost-effective and direct funding is needed to ameliorate global orthopaedic health [ 61 ]. Of the ten studies on surgical mission trips to Africa included in their review [ 61 ], none were in Tanzania, further highlighting the need for research in this specific population.

It is well-accepted that randomized clinical trials are the gold standard for assessing causal relationships in research [ 62 ]. However, this research methodology is often expensive and time-consuming [ 63 ]. In developing countries, various barriers exist to conducting high-quality research [ 64 ], such as lack of suitable research infrastructure, deficiency in policymakers understanding of the importance of research, and absence of research materials [ 65 – 67 ]. Historically, research in Tanzania has not been focused on the context-specific structural determinants of health and inequities, which warrants wider implementation of local investigator-initiated trials [ 64 , 67 ]. Further initiatives should focus on interdisciplinary collaboration in creating appropriate research infrastructure to better suit randomized controlled trials. As more experimental research is done in Tanzania, the management of orthopaedic conditions will be better understood to ultimately improve patient outcomes.

Most of the studies included were conducted by Tanzanian authors. Nevertheless, addressing orthopaedic and trauma care in other LMICs remains a global health priority. In LMICs, RTIs remains a significant health burden, due to similar factors discussed in this review [ 1 ]. Longitudinal partnerships and research that reflect the interests and goals of local populations in other LMICs are needed to further explore this topic, and ultimately improve global health outcomes [ 6 – 10 ].

Strengths and limitations

The major strength of this scoping review is its comprehensive overview of the literature. By including studies dating back to 1966, this review encapsulates the wide range of methodologies, focuses, and themes in orthopaedic and trauma research in Tanzania. This breadth of included studies allowed for a thorough review of trends and gaps in the literature, highlighting areas where future research is warranted. The main limitations are the lack of ability to perform a meta-analysis and lack of study quality (risk of bias) assessment.

The escalating orthopaedic and trauma crisis in Tanzania demands immediate attention and international intervention to mitigate the country’s massive health and economic burden. This scoping review offers a comprehensive analysis of orthopaedic and trauma research conducted in Tanzania from 1966 to 2023. Our findings highlight the prevalence of RTIs as a significant cause of injuries, emphasizing the pressing need for more effective interventions and health policies to reduce this burden. The notable scarcity of research on WRIs and randomized controlled trials indicates a significant gap in the existing literature, highlighting the need for high-quality research in these areas in Tanzania. A commitment to implementing sustainable orthopaedic and trauma care should be a local and global priority. This scoping review aims to catalyze further research endeavors and outreach initiatives in Tanzania’s orthopaedic and trauma sectors.

Supporting information

S1 checklist. prisma-scr checklist..

https://doi.org/10.1371/journal.pone.0304218.s001

S1 File. Search strategy.

https://doi.org/10.1371/journal.pone.0304218.s002

• 1. World Health Organization. Injuries and violence: the facts 2014 [Internet]. World Health Organization; 2014 [cited 2023 Aug 24]. 19 p. https://apps.who.int/iris/handle/10665/149798
• View Article
• PubMed/NCBI
• Google Scholar
• 8. World Health Organization. Local production and technology transfer to increase access to medical devices: addressing the barriers and challenges in low- and middle-income countries [Internet]. World Health Organization; 2012 [cited 2023 Aug 24]. iv, 144 p. https://apps.who.int/iris/handle/10665/336774
• 15. Rayyan—ai powered tool for systematic literature reviews [Internet]. 2021 [cited 2023 Aug 28]. https://www.rayyan.ai/
• 16. Google sheets: online spreadsheet editor | google workspace [Internet]. [cited 2023 Aug 28]. https://www.facebook.com/GoogleDocs/
• 29. Hardaker WM, Jusabani M, Massawe H, Pallangyo A, Temu R, Masenga G, et al. The burden of orthopaedic disease in sub-saharan africa: a focus on tanzania [Internet]. In Review; 2021 Apr [cited 2023 Aug 28]. https://www.researchsquare.com/article/rs-402380/v1
• 33. Clyde & Co LLP—Peter Kasanda. Lexology. 2014 [cited 2023 Sep 4]. An overview of the road infrastructure in Tanzania. https://www.lexology.com/library/detail.aspx?g=ba8c3064-6a64-4759-befc-2fecc5834b31
• 36. Kumar A. Understanding the emerging role of motorcycles in african cities: a political economy perspective. 2011 Apr [cited 2023 Sep 4]; http://hdl.handle.net/10986/17804
• 37. Second Decade of Action for road safety [Internet]. [cited 2023 Aug 28]. https://www.who.int/teams/social-determinants-of-health/safety-and-mobility/decade-of-action-for-road-safety-2021-2030
• 41. World health statistics 2013 [Internet]. [cited 2023 Aug 28]. https://www.who.int/publications-detail-redirect/9789241564588
• 43. Statista [Internet]. [cited 2023 Aug 28]. Tanzania: people in extreme poverty 2016–2025. https://www.statista.com/statistics/1230404/number-of-people-living-in-extreme-poverty-in-tanzania/
• 45. Abdalla S, Apramian SS, Cantley LF, Cullen MR. Occupation and risk for injuries. In: Mock CN, Nugent R, Kobusingye O, Smith KR, editors. Injury Prevention and Environmental Health [Internet]. 3rd ed. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2017 [cited 2023 Aug 28]. http://www.ncbi.nlm.nih.gov/books/NBK525209/
• 47. ILO Estimates Over 1 Million Work-Related Fatalities Each Year [Internet]. 1999 [cited 2023 Aug 28]. http://www.ilo.org/global/about-the-ilo/newsroom/news/WCMS_007969/lang--en/index.htm
• 51. BOA. Women in surgery–tanzania [Internet]. [cited 2023 Aug 28]. https://www.boa.ac.uk/about-us/diversity-and-inclusion/international-women-s-day/women-in-surgery-tanzania.html
• 52. AAOS. ORTHOPAEDIC PRACTICE IN THE U.S. 2018 [Internet]. AAOS Department of Clinical Quality and Value; 2018. https://www.aaos.org/globalassets/quality-and-practice-resources/census/2018-census.pdf
• 55. Doctors Without Borders—USA [Internet]. [cited 2023 Aug 28]. Tanzania. https://www.doctorswithoutborders.org/what-we-do/where-we-work/tanzania
• 59. World Population Prospects The 2015 Revision. Key Findings and Advance Tables. Population Division: Department of Economic and Social Affairs; 2015.
• 66. Exploring the current state of health research in sub-saharan africa [Internet]. [cited 2023 Aug 29]. https://www.rand.org/randeurope/research/projects/health-research-sub-saharan-africa.html

IJCRAR is now DOI (CrossRef) registered Research Journal. The DOIs are assigned to all published IJCRAR Articles.

Click here to current issue, volume 12 number 5                may 2024.

• Editorial Member Login

Quick Navigation

• Call for Papers
• Processing Charges
• Join with us
• Editorial Board

2024-Issues

• Volume 12-1 January
• Volume 12-2 February
• Volume 12-3 March
• Volume 12-4 April
• Volume 12-5 May

2023-Issues

• Volume 11-1 January
• Volume 11-2 February
• Volume 11-3 March
• Volume 11-4 April
• Volume 11-5 May
• Volume 11-6 June
• Volume 11-7 July
• Volume 11-8 August
• Volume 11-9 September
• Volume 11-10 October
• Volume 11-11 November
• Volume 11-12 December

2022-Issues

• Volume 10-1 January
• Volume 10-2 February
• Volume 10-3 March
• Volume 10-4 April
• Volume 10-5 May
• Volume 10-6 June
• Volume 10-7 July
• Volume 10-8 August
• Volume 10-9 September
• Volume 10-10 October
• Volume 10-11 November
• Volume 10-12 December

2021-Issues

• Volume 9-1 January
• Volume 9-2 February
• Volume 9-3 March
• Volume 9-4 April
• Volume 9-5 May
• Volume 9-6 June
• Volume 9-7 July
• Volume 9-8 August
• Volume 9-9 September
• Volume 9-10 October
• Volume 9-11 November
• Volume 9-12 December

2020-Issues

• Volume 8-1 January
• Volume 8-2 February
• Volume 8-3 March
• Volume 8-4 April
• Volume 8-5 May
• Volume 8-6 June
• Volume 8-7 July
• Volume 8-8 August
• Volume 8-9 September
• Volume 8-10 October
• Volume 8-11 November
• Volume 8-12 December
• View All Archives
• Advertise(IJCRAR)
• Terms of Use
• Copyright Form

© Copyright 2013-2016 all rights reserved by IJCRAR. Powered by 7Cube Technologies